Quantitative Magnetic Resonance Imaging of the Vasculature

ABSTRACT

A quantitative, ultrashort time to echo, contrast-enhanced magnetic resonance imaging technique is provided. The technique can be used to accurately measure contrast agent concentration in the blood, to provide clear, high-definition angiograms, and to measure absolute quantities of cerebral blood volume on a voxel-by-voxel basis.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 § 119(e) of U.S. ProvisionalApplication No. 62/196,692, filed on Jul. 24, 2015, entitled“Quantitative Imaging Modality for Blood Volume Fractions, ContrastAgent Concentration and Vessel Delineation Measurements in MagneticResonance Imaging,” and U.S. Provisional Application No. 62/322,984,filed on Apr. 15 2016, entitled “Quantitative Magnetic Resonance Imagingwith Magnetic Nanoparticles” the disclosures of which are herebyincorporated by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The invention was made with government support under Grant No.1U54CA151881 from the United States Department of Health and HumanServices, and was made with government support under Grant No. DGE096543from the National Science Foundation. The U.S. Government has certainrights in the invention.

BACKGROUND

Magnetic resonance angiography (MRA) is a known technique to delineatevasculature, particularly with the use of contrast agents (CA), whichprovide clear angiograms for diagnosing vascular diseases whileeliminating the risks of radiation, iodinated contrast, and arterialcatheterization.

The major types of angiographic sequences can be categorized asTime-of-Flight (TOF), Phase-Contrast (PC), susceptibility-weightedimaging (SWI) angiography, contrast enhanced MR angiography (CE MRA) andquantitative susceptibility mapping (QSM). TOF relies on saturation oftissue signal intensity over multiple excitations and blood becomesbright as it moves from a previously unexcited region into the volume ofexcitation, since it has a fresh magnetization. TOF imaging is usually ashort TR gradient echo sequence (GRE) and is T1-weighted. Addinggadolinium contrast to the blood further enhances the T1-contrast. It ispossible to suppress the venous vessels in TOF angiography by saturatingblood signal superior to the imaging slabs. Black blood (BB) contrast isalso employed sometimes using a spin-echo (SE) sequence in which theblood appears dark because it moves away from the excitation slab beforethe echo can be refocused. TOF imaging is inherently good for measuringlarge arteries and veins. In CE MRA, a fast GRE technique such as aT1-weighted spoiled gradient echo (SPGR) is used to get T1-weightedimages with structural information. PC imaging is based on the fact thata gradient magnetic field will affect the phase of blood differentlythan static tissue. PC imaging typically employs a GRE sequence and hasthe additional benefit of being able to measure the flow velocity ofblood by mapping that velocity with pulsed gradients. SWI and QSM bothrely on T2*-weighted imaging. SWI relies on attenuating magnitudemeasurements with a phase mask; QSM attempts to estimate quantitativevalues for magnetic susceptibility at each voxel. Both use gradient echoT2*-weighted images at multiple echoes for calculations. SWI tends tooverestimate the width of vessels because of blooming. With QSM it'sdifficult to distinguish between veins and tissue.

Gadolinium based CAs (GBCAs) are used exclusively in standard clinicalprocedure for their superior r₁ relaxivity, and also because they arethe only FDA approved pharmaceutical explicitly for MRA. They have someserious limitations including nephrotoxicity (contrast-enhanced MRA withGBCA cannot be done safely on renally impaired patients), leakage out ofthe vascular compartment (except gadofosveset trisodium), and shortblood half-life (˜30 minutes). Thus, there is a major need for aneffective MRA modality, particularly for renally impaired patients, withless toxicity while retaining superior contrast properties.

Superparamagnetic iron-oxide nanoparticles (SPIONs) have been recognizedto be highly biocompatible with minimal toxicity, but their use has beenlimited by the commonly employed T2-weighted imagining techniques whichproduce negative contrast or poorer contrast in T1-weighted images.However, imaging using ferumoxytol is known to produce strictly vascularsignal changes, which has led to interest in using this product to mapblood volume in areas like the brain where quantitative vascularmeasurements are important for planning tumor biopsy locations.

MRA has been used to study a variety of neuro-physiological phenomena,such as blood velocity and volume flow rate using phase contrast (PC)MRA, where quantitative functional information is often sought after ona voxel-by-voxel basis with techniques that measure changes in abaseline signal based on cerebral activity for functional MRI (fMRI).One known fMRI tool is the blood oxygenation level dependent (BOLD)technique. The BOLD technique measures changes in a baseline signal dueto variations in the oxygenated and deoxygenated hemoglobin. While MRAand fMRI methods have proven useful for measuring semi-quantitativedynamic information based on percent changes in an arbitrary MR signal,the resting state percent cerebral blood volume (CBV) is indicative ofthe overall health, as it is well established that many neuropathiesresult in vascular abnormalities.

Currently, dynamic susceptibility contrast (DSC) MRI is commonly usedfor measuring CBV values, but it requires accurate determination of thearterial input function (AIF), or GBCA concentration versus time curve,which is typically 15-30% inaccurate. Furthermore a fast acquisitionprotocol (such as echo-planar imaging (EPI)) must be employed, whichinherently limits both the spatial resolution and the signal-to-noiseratio (SNR), and is also prone to artifacts including image warping. Ithas been shown that CBV measurements with DSC-MRI are even moreinaccurate in ischemic tissue because of late, unpredictable arterialarrival of CA.

Other techniques for measuring the CBV, such as steady-statesusceptibility contrast mapping (SSGRE), steady state CBV (SS CBV), andΔR2, all utilize T₂ and T₂* effects, which are susceptible to intra- andextra-voxular dephasing as well as flow artifacts. They all operate onthe central assumption that a linear relationship exists between the CAconcentration and the transverse relaxation rate and that it isspatially uniform, whereas in the presence of bulk blood, such as in thesuperior sagittal sinus, the relationship is not linear, but quadratic.Usually ˜1 mm³ isotropic resolution is utilized to compensate for areduction in partial volume effects, while maintaining enough signalfrom T₂- or T₂*-weighted images for acquisition. IRON fMRI using SPIONsis a promising tool for CBV measurements, with the ability to optimizeblood magnetization at any echo time, enabling high detection power andthe use of short echo times. IRON fMRI is T₂* weighted, requires high CAdoses, and is sensitive to extra-vascular space.

Accordingly, these prior art techniques have been unable to measureabsolute functional qualities in the brain.

SUMMARY OF THE INVENTION

In contrast to the prior art techniques, a quantitative ultra-short timeto echo technique (termed QUTE-CE) is provided that can be successfullyapplied to accurately measure CA concentration in the blood, to provideclear, high-definition angiograms, and to measure absolute quantities ofCBV on a voxel-by-voxel basis.

Other aspects of the method and system include the following:

1. A method of positive-contrast magnetic resonance imaging of asubject, comprising:

introducing a paramagnetic or superparamagnetic contrast agent into aregion of interest in the subject;

applying a magnetic field to the region of interest;

applying a radio frequency pulse sequence at a selected repetition time(TR) and at a magnetic field gradient to provide a selected flip angleto excite protons in the region of interest, wherein the repetition timeis less than about 10 ms, and the flip angle ranges from about 10° toabout 30°;

measuring a response signal during relaxation of the protons at aselected time to echo (TE) to acquire a T₁-weighted signal from theregion of interest, wherein the time to echo is an ultra-short time toecho less than about 300 μs; and generating an image of the region ofinterest.

2. The method of item 1, wherein the acquired signal is representativeof a concentration of the contrast agent in the region of interest.3. The method of any of items 1-2, wherein the acquired signal isrepresentative of a blood volume in the region of interest.4. The method of item 3, wherein the blood volume fraction comprises acerebral blood volume fraction or a total blood volume fraction.5. The method of any of items 1-4, wherein the acquired signal comprisesan absolute quantitative signal.6. The method of any of items 1-5, wherein the signal is acquired beforemagnetization of tissue in the region of interest in a transverse planedephases.7. The method of any of items 1-6, wherein the signal is acquired beforea T₂* decay becomes greater than 2%, or greater than 10%.8. The method of any of items 1-7, wherein the signal is acquired beforecross talk between voxels occurs.9. The method of any of items 1-8, further comprising setting the timeto echo (TE) to a value from about 1 μs to about 300 μs.10. The method of any of items 1-9, further comprising setting the timeto echo (TE) to less than 180 μs, 160 μs, 140 μs, 120 μs, 100 μs, 90 μs,80 μs, 70 μs, 60 μs, 50 μs, 40 μs, 30 μs, 20 μs, or 10 μs.11. The method of any of items 1-10, further comprising setting the timeto echo (TE) to less than a time in which blood volume displacement inthe region of interest is about one order of magnitude smaller than avoxel size.12. The method of any of items 1-11, further comprising setting therepetition time (TR) to a value from about 2 to about 10 ms.13. The method of any of items 1-12, further comprising setting the flipangle to a value from about 10° to about 25°.14. The method of any of items 1-13, wherein the image of the region ofinterest has a contrast to noise ratio of at least 4, at least 5, atleast 10, at least 15, at least 20, at least 30, at least 40, at least50, or at least 60.15. The method of any of items 1-14, wherein the contrast to noise ratiois determined between the image of the region of interest and apre-contrast image of the region of interest generated prior tointroduction of the contrast agent.16. The method of any of items 1-14, wherein a contrast to noise ratiois determined between tissue and blood fractions of the region ofinterest.17. The method of any of items 1-16, further comprising measuring theresponse signal along radial trajectories in k-space.18. The method of any of items 1-17, further comprising measuring theresponse signal along orthogonal trajectories in k-space.19. The method of any of items 1-18, further comprising saturating theregion of interest with signal pulses at the repetition time (TR).20. The method of any of items 1-19, further comprising acquiring apurely T1-weighted signal.21. The method of any of items 1-20, wherein the magnetic field has astrength ranging from 0.2 T to 14.0 T.22. The method of any of items 1-22, wherein the region of interestcomprises a volume fraction occupied by blood and a volume fractionoccupied by tissue; and

further comprising determining the volume fraction occupied by blood.

23. The method of item 22, wherein determining the volume fractionoccupied by blood comprises:

prior to introducing the contrast agent to the region of interest,applying the radio frequency pulse sequence at the selected TR to exciteprotons in the region of interest, and measuring a response signalduring relaxation of the protons at the selected TE to acquire a signalfrom the region of interest; and

comparing signal intensities of the region of interest prior tointroducing the contrast agent and after introducing the contrast agent.

24. The method of any of items 1-23, wherein an image intensity of theimage is proportional to a concentration of the contrast agent in theregion of interest.25. The method of any of items 1-24, wherein the image depicts athree-dimensional representation of the region of interest.26. The method of any of items 1-25, wherein the image depicts a volumeof the region of interest.27. The method of any of items 1-26, wherein the image depicts atwo-dimensional representation of the region of interest.28. The method of any of items 1-27, wherein the image depicts a sliceof the region of interest.29. The method of any of items 1-28, wherein the contrast agent isintroduced in the region of interest at a concentration of 0.1 to 15mg/kg.30. The method of any of items 1-29, wherein the paramagneticnanoparticles comprise iron oxide nanoparticles, gadolinium chelates, orgadolinium compounds.31. The method of item 30, wherein the iron oxide nanoparticles compriseFe₃O₄ (magnetite), γ-Fe₂O₃ (maghemite), α-Fe₂O₃ (hematite).32. The method of item 30, wherein the iron oxide nanoparticles compriseferumoxytol, ferumoxides, ferucarbotran, or ferumoxtran.33. The method of any of items 30-32, wherein the iron oxide particlesare coated with a carbohydrate.34. The method of any of items 30-33, wherein the iron oxidenanoparticles have a hydrodynamic diameter of about 25 nm, measured withdynamic light scattering.35. The method of any of items 30-33, wherein the iron oxidenanoparticles have a diameter from about 1 nm and about 999 nm, or fromabout 2 nm and about 100 nm, or from about 10 nm and about 100 nm,measured with dynamic light scattering.36. The method of item 30, wherein the gadolinium compounds comprisegadofosveset trisodium, gadoterate meglumine, gadoxetic acid disodiumsalt, gadobutrol, gadopentetic dimeglumine, gadobenate dimeglumine,gadodiamide, gadoversetamide, or gadoteridol.37. The method of any of items 1-36, further comprising calibrating amagnetic resonance imaging device to determine the selected TR and theselected TE and a selected flip angle.38. The method of item 37, wherein an intensity of the acquired signalis a function of a time to echo (TE), a repetition time (TR), and a flipangle (θ).39. The method of any of items 37-38, wherein the intensity of theacquired signal is a function of a longitudinal relaxation time T₁ and atransverse relaxation time T₂*.40. The method of any of items 37-39, wherein the intensity of theacquired signal is a function of a calibration constant K dependent on acoil of the magnetic resonance imaging device and a proton density ρ ofthe vascular region.41. The method of any of items 37-40, wherein the intensity of theacquired signal is a function of magnetic flux densities B₀ and B₁(+/−).42. The method of any of items 1-41, wherein the subject is a human or anon-human animal.43. The method of any of items 1-42, wherein the region of interest is avascular region, a tissue compartment, an extracellular space, or anintracellular space containing the contrast agent.44. The method of any of items 1-43, wherein the region of interest is abrain, a kidney, a lung, a heart, a liver, a pancreas, or a tumor, or aportion thereof.45. The method of any of items 1-44, further comprising diagnosing adisease or condition, the disease or condition selected from the groupconsisting of a neurodegenerative disease, neuropathy, dementia,Alzheimer's disease, cancer, kidney disease, lung disease, heartdisease, liver disease, ischemia, abnormal vasculature,hypo-vascularization, hyper-vascularization, and nanoparticleaccumulation in tumors, and combinations thereof.46. A system for magnetic resonance imaging of a region of interest of asubject, comprising:

a magnetic resonance imaging device operative to generate signals forforming a magnetic resonance image of a region of interest, and

one or more processors and memory, and computer-executable instructionsstored in the memory that, upon execution by the one or more processors,cause the system to carry out operations, comprising:

operating the magnetic resonance imaging device with a radio frequencypulse sequence comprising:

-   -   a selected repetition time (TR) and at a magnetic field gradient        to provide a selected flip angle to excite protons in the region        of interest within a magnetic field generated by the magnetic        resonance device, wherein the repetition time is less than about        10 ms, and the flip angle is from about 10° to about 30°, and    -   a selected time to echo (TE) to acquire a T₁-weighted signal        from the region of interest, wherein the time to echo is an        ultrashort time to echo less than about 300 μs.        47. The system of item 46, wherein the time to echo (TE) is from        about 1 μs to about 200 μs.        48. The system of any of items 46-47, wherein the time to echo        (TE) is less than 180 μs, 160 μs, 140 μs, 120 μs, 100 μs, 90 μs,        80 μs, 70 μs, 60 μs, 50 μs, 40 μs, 30 μs, 20 μs, or 10 μs.        49. The system of any of items 46-48, wherein the repetition        time (TR) is from about 3.5 to about 10 ms.        50. The system of any of items 46-49, wherein the flip angle is        from about 10° to about 25°.        51. The system of any of items 46-50, wherein the magnetic field        strength is from about 0.2 T to about 14.0 T.        52. A method of determining a blood volume fraction in a region        of interest of a subject comprising:

generating a first image of the region of interest;

introducing a paramagnetic or superparamagnetic contrast agent into aregion of interest in the subject;

applying a magnetic field to the region of interest;

applying a radio frequency pulse sequence at a selected repetition time(TR) and at a magnetic field gradient to provide a selected flip angleto excite protons in the region of interest, wherein the repetition timeis less than about 10 ms, and the flip angle ranges from about 10° toabout 30°;

measuring a response signal during relaxation of the protons at aselected time to echo (TE) to acquire a T₁-weighted signal from theregion of interest, wherein the time to echo is an ultra-short time toecho less than about 300 μs;

generating a second image of the region of interest;

determining a blood volume fraction in the region of interest.

53. The method of item 52, wherein the region of interest comprises avascular region having a volume fraction occupied by blood and a volumefraction occupied by tissue.54. The method of any of items 52-53, wherein determining the bloodvolume fraction comprises comparing signal intensities of the region ofinterest prior to introducing the contrast agent and after introducingthe contrast agent.55. The method of any of items 52-54, determining the blood volumefraction comprises determining a difference in total signal intensitiesbetween the first image and the second image and determining adifference in blood signal intensities between the first image and thesecond image, wherein the blood volume fraction comprises a ratio of thetotal signal intensity difference to the blood signal intensitydifference.56. A system for magnetic resonance imaging of a region of interest of asubject, comprising:

a magnetic resonance imaging device operative to generate signals forforming a magnetic resonance image of the region of interest, and

one or more processors and memory, and computer-executable instructionsstored in the memory that, upon execution by the one or more processors,cause the system to carry out operations comprising the steps of any ofitems 1-45 and 52-55:

57. A non-transitory computer readable medium with computer executableinstructions stored thereon executed by a processor to perform themethod of any of items 1-45 and 52-55.

DESCRIPTION OF THE DRAWINGS

The invention will be more fully understood from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1A is a schematic depiction illustrating acquisition of an MRIsignal according to an embodiment of the present UTE technique and aprior art regular TE technique.

FIG. 1B illustrates images of 50 ml vials of ferumoxytol-doped animalblood measured at 3T with concentration decreasing from left to right(scan parameters: Siemens TrioTim, 3DUTE sequence TR=2.79 ms, TE=70 μs(top image), and spin-echo image with TR=6000 ms, TE=13.8 ms (bottomimage)).

FIG. 2 is a flowchart illustrating steps in performing a quantitativeultrashort TE contrast-enhanced MRI scan according to one embodiment.

FIG. 3 is a graph of relaxation rate measurements R₁ and R₂ as afunction of concentration (squares designate mouse blood performed withone blood phantom at a time; circles designate calf-blood performed withmultiple vials present; diamonds designate calf-blood in a separateexperiment with multiple vials present to ensure no coagulation presentin blood).

FIG. 4 illustrates radial 3DUTE images of mouse thoracic region. (a)Pre- and (b) post-contrast image of whole upper body of a mouse (5 cm³isotropic FOV, 250 μm3 isotropic resolution, TE=13 μs, TR=8 ms, FA=16°).(c) Pre- and (d) post-contrast images of thoracic region (3 cm³isotropic FOV, 150 μm³ isotropic resolution, TE=13 μs, TR=3.5 ms,FA=20°).

FIG. 5 illustrates optimization of QUTE-CE image acquisition parameters.(a) Heatmap of the standard error in concentration as a function of θ,TE, and TR. The lowest error is observed at θ=20°, TE=13 μs, and TR=3.5ms. (b) Variation in the measurement residual by changing TR, with theoptimal curve shown with a heavier line. Fixed parameters: θ=20° andTE=13 μs. (c) Variation in the measurement residual by changing TE, withthe optimal curve shown with a heavier line. Fixed parameters: θ=20° andTR=3.5 ms. (d) Agreement between measured signal intensity (circles) andtheory (dashed line) under optimal image acquisition parameters forsamples with known concentrations. (e) Measured signal-to-noise (SNR,black) and contrast-to-noise (CNR, grey) ratio as a function offerumoxytol concentration under optimal image acquisition parameters.

FIG. 6 illustrates time-corrected Signal to Noise Ratio and Contrast toNoise Ratio, including the time correction factor

$\left( \frac{1}{\sqrt{TR}} \right).$

(a) A heat map of the SNR and (b) CNR for given TR, TE, θ, andconcentration values.

FIG. 7 illustrates verification of optimization experiments withcalf-blood (FIG. 5) double-checked with mouse blood at similar TE and TRvalues for θ=20°. (a) The average absolute error in concentration byQUTE-CE measurements for mouse blood of phantom concentrations 50, 75,100, 125, 150 and 175 μg/ml, and (b) calf-blood on phantoms ofconcentrations θ, 50, 100, 150 μg/ml; calf-blood data is the same forθ=20°.

FIG. 8 illustrates how ferumoxytol-doped calf-blood (1% heparin)phantoms were positioned for the in vitro QUTE-CE calibration scans(α-d) and unknown in vitro concentrations scans (e-i). Images arecentered axial slices from the 3D-UTE images. (α-d) show scans of singlevials, used for calibrating Kρ. The average value K was subsequentlyused for in vitro and in vivo calculations for concentrations.Concentrations are: (a) 0 μg/ml (b) 50 μg/ml (c) 100 μg/ml and (d) 150and 0 μg/ml; (e-i) show concentrations and vial locations in vitroexperiments of the following concentrations: (e) 2, 1, 0 μg/ml (f) 6, 4,3 μg/ml (g) 16, 12, 8 μg/ml (h) 48, 32, 24 μg/ml (i) 128, 96, 64 μg/ml.All scans were performed with an accompanied 0 μg/ml vial as seen inimages (except a).

FIG. 9 illustrates in vitro results on ferumoxytol concentrationmeasurements. (a) Measured Kρ values (circles) and the calibration value(- - -) set to the average value from doped vials demonstrates that Kρis constant for the concentration range of interest at optimal imagingparameters (θ=20°, TE=13 μs, TR=4 ms). (b) Agreement between measuredand actual ferumoxytol concentration for phantoms containingconcentrations of ferumoxytol (circles). Line y=x (- - -) is shown forcomparison. Inset, Linear regression residuals about y=x forexperimental measurements. (c) 2D positive-contrast slice image from a3-D optimized UTE pulse sequence. Phantoms contain 128, 96, 64 and 0μg/ml ferumoxytol respectively (counterclockwise). (d) Correspondingferumoxytol concentration as calculated by theory. (e) Concentrationprofile along the z-axis of the doped phantoms demonstrates the effectof B₁ ⁺ inhomogeneity on concentration measurements. Measurements arealways most precise in the center (z-axis slice position=0).

FIG. 10 illustrates the effect of B₁ inhomogeneity on concentrationmeasurements. (a) The effect of inhomogeneity in B₁ on concentrationmeasurements has been determined by drawing ROIs to measureconcentration along phantoms containing ferumoxytol-doped blood fromFIG. 8. (b) The effect has been further quantified at a distance of −5mm per concentration. The effect is more pronounced at higherconcentrations.

FIG. 11 illustrates the measurement of ferumoxytol concentration invivo. (a) Representative pre-contrast QUTE-CE image rendered with3DSlicer, demonstrating that the mouse interior is invisible. (b)Corresponding post-contrast image of a mouse treated with a 0.4-0.8 mgbolus of ferumoxytol, showing clear delineation of the thoracicvasculature. (c) Automated segmentation, centered at one standarddeviation of the measured mean, allows reconstruction of regionscontaining the contrast agent. (d) Agreement between ferumoxytolconcentration measured by QUTE-CE (in vivo) and ICP-AES (ex vivo, ofdrawn blood). Insert, measured residuals show excellent agreement, withan average of 7.07% error (6.01±4.93 μg/ml, maximum 13.5 μg/ml error).Insert, representative 2-D slice positive-contrast image demonstratingROI placement for the QUTE-CE image analysis. (e) Measured ferumoxytolblood concentration as a function of time, demonstrating sufficientaccuracy to permit calculation of contrast agent half-life by imagingalone.

FIG. 12 illustrates raw ICP-AES data. (a) Standard curve for saltconcentration; solid line is a linear regression with r²=1.000 andslope=1.02. (b) 1% heparin calf-blood; solid line is a linear regressionwith r²=0.996 and slope=51.03. (c) Blood drawn directly from mice afterimaging time-points; solid lines are fits using a pooled slope (17.11)from all five data sets (average r²=0.978) and using the averageintercept from each individual set of set (n=3).

FIG. 13 illustrates QUTE-CE tumor contrast. (a) Pre-contrast QUTE-CE 2Dslice of a PC3 tumor image. (b) Immediately after contrastadministration. (c) 24 h after contrast administration. (d,e,f) 3Drenderings of tumor using 3D slicer. The same linear opacity gradient isused to fairly render all three images. The accompanying histogramsunderneath the 3D images demonstrate the evolution of voxel intensitydistribution from d-f; the fit line on d is Gaussian.

FIG. 14 illustrates tumor blood volume (TBV) imaging with QUTE-CE. (a)3D rendering with Vivoquant software (Invicro, Boston, Mass.) of a PC3tumor anatomically with QUTE-CE overlay. (b) Subsequent TBV image shownhere as 3D opaque with two cuts to visualize the interior, rendered in3DSlicer.

FIG. 15 illustrates a comparison between T₁, T₂ and QUTE-CEmeasurements, showing example pre- and 24 h post-contrast images, withno nanoparticles and accumulated nanoparticles but no vascularnanoparticles respectively, as well as subsequent difference images areshown for the three imaging modalities (units on the scales aremilliseconds for T₁ and T₂ and absolute intensity for UTE.

FIG. 16 illustrates reference images for ROI analysis in which PC3tumors are numbered 1-5 (upper left corner of each image set) (Sets of 4images are displayed per tumor, and the pattern for which they aredisplayed is labeled in text for Tumor 1. T₂ and QUTE-CE pre-contrastimages are displayed above pre-contrast images, having beenco-registered such that the same 2D plane is shown for both pre- andpost-contrast. ROIs were drawn independently on T₂ and QUTE-CE images,then a common mask was taken to produce fair results, which aretabulated in Table 1.

FIG. 17 illustrates contrast comparison for QUTE-CE MRI to ΔT₁ and ΔT₂imaging for CNR comparison for the five PC3 tumors displayed in FIG. 16.Error bars are one standard deviation. This CNR in (a) is raw and (b)takes into account the scan time and total volume scanned as in Equation10.

FIG. 18 illustrates QUTE-CE (a) pre-contrast (left) and post-contrast(right) MIP images of a Sprague Dawley rat brain rendered in Paravision5.1 with contrast and image parameters. Bright vessels pre-contrast arefrom TOF effects from incomplete saturation of arterial blood, and areshown to be limited to the periphery and not encountered in the (b)cropped brains from (a) rendered with 3DSlicer using the same linearopacity gradient for pre- and post-contrast images showing a nominalbackground signal pre-contrast.

FIG. 19 illustrates GBCA PC MRA vs. SPION-enhanced QUTE-CE in rat headTop row: QUTE-CE MRI in rat head with 2× clinical dose of ferumoxytol.Bottom row: phase contrast angiography with 2× clinical dose ofmultihance. 3D MIPs are rendered in Paravision 5.1. Dorsal (D), Ventral(V), Left (L), Right (R), Anterior (A) and Posterior (P) sides arelabeled on the UTE images. The Paravision gradient echo FC2D sequencewith 200×200 matrix, 200 slices of 0.3 mm thickness and −0.15 mm slicegap, resolution of 0.15 mm³ isotropic, TE=4 ms, TR=18 ms, FA=80°, 2averages, 3 cm³ isotropic FOV, 18 m 0 s total scan time. For QUTE-CEMRI, the 3DUTE Bruker pulse sequence was used with 200×200×200 matrix,TE=0.013 ms, TR=4 ms, FA=20°, 2 averages, 16 m 33 s total scan time.

FIG. 20 illustrates UTE images of a rat blood phantom.Homogeneity-corrected UTE image of a dead rat blood phantom in the (a)axial, (b) sagittal and (c) coronal slices of one rat's cranial spacefilled with his own excised contrast-enhanced blood post-mortem. (d)Homogeneity corrected (bottom) and uncorrected (top) sagittal sliceswith exaggerated intensity scale for visual comparison.

FIG. 21 illustrates homogeneity correction along z-axis of image datausing blood phantoms (a) The intensity profile for 10 dead rat bloodphantoms was extrapolated along the z-axis and fit with a 6^(th) degreepolynomial function to normalize the intensity pattern for both channelsseparately. (b) The subsequent corrected magnitude image showedameliorations in the intensity values profiles for all voxels within thebrain region, which should be a constant value with Gaussian noise.

FIG. 22 illustrates in vivo vs. in vitro measurements of rat whole bloodintensity. Regions of interest were drawn along the superior sagittalsinus of anesthetized, homogeneity-corrected 3DUTE in vivo images usingthe LevelTracingEffect tool in 3DSlicer. The mean of the Gaussian fit inthat region of interest was compared to the mean value found ex vivoboth absolutely (a) and percent difference (b).

FIG. 23 illustrates resting state capillary blood volume atlas. (a) Ananatomical atlas consisting of 174 regions was used to construct avascular atlas of CBV from (b) fitting the first peak of each region toa Gaussian, which should primarily consist of capillary-filled voxels.The three regions displayed in the histograms demonstrate the variety ofblood distributions found throughout the brain—some filled primarilywith capillaries (low CBV), some rather heterogeneous (medium CBV) andsome rather bio-modal (large and small vessels). (c) The capillary CBVis shown for select slices of the atlas.

FIG. 24 illustrates functional changes in CBV compared to baselinemeasurement. Steady-state functional measurements of a) CBV changecomparing 5% CO₂-Challenge to awake baseline and b) CBV change comparingto 3% isoflurane to awake baseline. Positive values denote greater CBVthan baseline and negative values denote a lesser CBV than baseline.Values are shown as absolute percent CBV.

DETAILED DESCRIPTION OF THE INVENTION

A quantitative, ultra-short time to echo (TE), contrast-enhancedmagnetic resonance imaging (MRI) technique utilizing ultrashort time toecho (UTE) sequences is provided. The UTE limitssusceptibility-dependent signal dephasing by giving perivasculareffects, extravoxular susceptibility artifacts, and flow artifacts alltypically associated with T₂ weighted imaging negligible time topropagate, and also limits the influence of physiological effects, suchas blood flow, by saturating a three-dimensional (3D) volume withnon-slice selective RF pulses at low repetition time (TR) to create asteady-state signal between TRs, and then by acquiring signals atultra-short TE values before blood can be displaced between excitationand measurement. This results in snapshots of the vasculature that areindependent of flow direction or velocity, arterial or venous systems,or vessel orientation. With optimized pulse sequences (TE, TR, flipangle (FA)), completely T₁-weighted images can be acquired with signalpredicted by the Spoiled Gradient Echo (SPGR) equation as a function ofconcentration.

More particularly, a paramagnetic or super paramagnetic contrast agentin introduced into a region of interest (ROI) in a subject, and a staticmagnetic field, using any suitable magnetic resonance imaging (MRI)machine, is applied to the region of interest. A radio frequency pulsesequence is applied at a repetition time (TR) and at a magnetic fieldgradient to provide a selected flip angle (θ) to excite protons in thevascular region. In some embodiments, the repetition time TR is lessthan about 10 ms. In some embodiments, TR is from about 2 to about 10ms. In some embodiments, TR is less than 9 ms, less than 8 ms, less than7 ms, or less than 6 ms. In some embodiments, the region of interest issaturated with signal pulses at the repetition time (TR). In someembodiments, the flip angle θ ranges from about 10° to 30°. In someembodiments, θ is from about 10° to about 25°.

A response signal is measured during relaxation of the protons at aselected time to echo (TE) to acquire a T₁-weighted signal from theregion of interest. An image of the region of interest can be generatedfrom the received response signal. In some embodiments, the time to echoTE is an ultra-short time to echo (UTE) less than about 300 μs. In someembodiments, the ultrashort time to echo (TE) is from about 1 μs toabout 200 μs. In some embodiments, the TE is less than 180 μs, less than160 μs, less than 140 μs, less than 120 μs, less than 100 μs, less than90 μs, less than 80 μs, less than 70 μs, less than 60 μs, less than 50μs, less than 40 μs, less than 30 μs, less than 20 μs, or less than 10μs. In some embodiments, the TE is less than a time in which bloodvolume displacement in a vascular region of interest is about one orderof magnitude smaller than a voxel size. In some embodiments, the signalis acquired before magnetization of tissue in a region of interest in atransverse plane dephases. In some embodiments, the signal is acquiredbefore a T₂* decay becomes greater than 2%, or greater than 10%. In someembodiments, the signal is acquired before cross talk between voxelsoccurs.

FIG. 1A is a schematic depiction illustrating the difference between MRIsignal intensity when a UTE pulse technique as described herein is usedcompared to a prior art regular TE technique. UTE produces positivecontrast images because of T₁ recovery, illustrated in the top image ofFIG. 1B, whereas the prior art TEs produce negative contrast images,illustrated in the bottom image of FIG. 1B. The images in FIG. 1B are of50 ml vials of ferumoxytol-doped animal blood measured at 3.0 T withconcentration decreasing from left to right. The dotted line depicts thepresence of the highest ferumoxytol concentration vial in the regular TEimage, which has disappeared completely due to signal loss. Note thatthe regular TE image is a spin-echo T₂-decay; signal loss would beconsiderably higher without the echo.

The UTE technique described herein is advantageous for a variety ofreasons. For example, the technique is quantitative, leading to directassay of the CA concentration for quantitative MRI. There are noreported techniques that can potentially make absolute measurements inCBV throughout the brain. In some embodiments, the acquired signal isrepresentative of a concentration of the contrast agent in the region ofinterest. In some embodiments, the acquired signal comprises an absolutequantitative signal.

More particularly, T₁-weighted ‘snapshot’ images of vasculaturecontaining CA can be obtained in vivo with UTE by selection of the imageacquisition parameters. This is atypical in MRI, in which image contrastis usually only modified in already visible regions by CA. This isexemplified in FIG. 4, in which a mouse is nearly invisible without CA(FIG. 4(a),(c)), but after intravenous administration of a clinicallyrelevant dose of ferumoxytol, the blood becomes bright (FIG. 4(b)(d)).Images are rendered with raw intensities without image subtraction sothat the rendering is a completely fair comparison. In FIG. 4,time-of-flight effects are present only at the edges of the image,because the incoming blood contains fresh magnetization until it iscompletely saturated. However, in the bulk of the image, signalintensity is insensitive to flow, because blood displacement between TRand TE is orders of magnitude less than the voxel size (<1% of voxelvolume displaced for 100 mm/s flow). The QUTE-CE technique is thusadvantageous in that primarily vasculature is present in post-contrastimages without need for image subtraction, though subtraction ofprecontrast can be used. Factors which are usually important in otherMRA techniques, such as vessel orientation with respect to the slicedirection, or image subtraction on first-pass, are not important usingthis technique, and all vasculature is visible, including arterial andvenous.

The UTE technique described herein can lead to positive contrast imagesof the vasculature with very high contrast-to-noise ratio (CNR) andsignal-to-noise ratio (SNR). In some embodiments, the contrast to noiseratio is at least 4, at least 5, at least 10, at least 15, at least 20,at least 30, at least 40, at least 50, or at least 60.

The vascular-tissue signal contrast is very high, since there is minimalleakage from the vascular compartment due to the nanoparticle nature ofthe CA. Vessel wall and form are clearly delineated, as opposed to, forexample, time-of-flight (ToF) MRA and phase contrast (PC) MRA.

When superparamagnetic iron oxide nanoparticles (SPIONs) are used as thecontrast agent, the use of Gd-based CA, which can lead tonephrotoxicity, is avoided. SPION formulations typically have a longplasma half-life of nearly 12 hours in humans (˜6 hours in rats), sothat data acquisition is not limited by first-pass clearance, as withGd-based CAs.

The technique can achieve purely T₁-weighted angiography and cerebralblood volume, in which susceptibility effects are minimized. Theultra-fast acquisition (that is, ultra-short time to echo) minimizesphysical issues that become more significant as time goes on: floweffects, extravoxular susceptibility effects, dephasing of transversemagnetization (T₂* effects), and the like. In this regime the spoiledgradient (SPGR) equation directly applies, enabling quantification ofCA, described further below.

All blood containing regions are equally visible, with signal intensityproportional to both CA concentration and partial blood volume in thevoxel. The signal is insensitive to flow, which subsequently eliminatesvessel orientation dependence.

The UTE technique can utilize FDA approved pharmaceuticals such asferumoxytol and gadofoveset trisodium (commercially available asABLAVAR®) and can be implemented on existing clinical and pre-clinicalscanners. It is comparable to CT and PET, while avoiding harmfulradiation.

The UTE technique can be used to provide an effective magnetic resonanceangiography (MRA) modality, with less toxicity if SPIONS are used, whileretaining superior contrast properties. The present technique can beused to measure absolute quantities of cerebral blood volume on avoxel-by-voxel basis. In some embodiments, the acquired signal isrepresentative of a blood volume in the vascular region of interest. Insome embodiments, the blood volume fraction is a cerebral blood volumefraction or a total blood volume fraction. The present technique can beused for functional imaging of brain tissue, in which the health ofbrain tissue can be assessed for indications of disease as well asquantification of disease progression and to provide specific andquantitative spatial information of regional neuropathy, resulting inimproved understanding of neurodegenerative pathogenesis.

The technique described herein can be used to generate images of aregion of interest in humans and in non-human animals. In someembodiments, the region of interest can be a vascular region, a tissuecompartment, an extracellular space, or an intracellular space. In someembodiments, the region of interest can be a brain, a kidney, a lung, aheart, a liver, a pancreas, or a tumor, or a portion thereof.

The technique described here can be used in the diagnosing of a diseaseor condition. The disease or condition can be a neurodegenerativedisease, neuropathy, dementia, Alzheimer's disease, cancer, kidneydisease, lung disease, heart disease, liver disease, cardiac diseases orareas around the aorta, ischemia, abnormal vasculature,hypo-vascularization, hyper-vascularization, nanoparticle accumulationin tumors, plaques, bleeding, macrophages, inflammation, or areas aroundimplants or stents or combinations thereof.

The UTE technique described herein can be used with any paramagnetic orsuperparamagnetic contrast agent (CA). The technique is particularlyuseful with superparamagnetic iron oxide nanoparticles (SPIONs), whichleads to quantifiable vascular images with superior clarity anddefinition.

In some embodiments, the contrast agent is iron oxide nanoparticles. Insome embodiments, the iron oxide nanoparticles are Fe₃O₄ (magnetite),γ-Fe₂O₃ (maghemite), α-Fe₂O₃ (hematite). In some embodiments, the ironoxide nanoparticles are ferumoxytol, ferumoxides (e.g., FERIDEX®),ferucarbotran (e.g., RESOVIST®), or ferumoxtran (e.g., COMBIDEX®). Insome embodiments, the iron oxide particles are coated with acarbohydrate. In some embodiments, the iron oxide nanoparticles have ahydrodynamic diameter of about 25 nm, measured with dynamic lightscattering (DLS). In some embodiments, the iron oxide nanoparticles havea diameter from about 1 nm to about 999 nm, or from about 2 nm and 100nm, or from about 10 nm to about 100 nm, measured with dynamic lightscattering (DLS).

In some embodiments, the contrast agent is a superparamagnetic ironoxide nanoparticle (SPION). In some embodiments, the SPION isferumoxytol. Ferumoxytol is an iron-oxide nanopharmaceutical approved bythe Food and Drug Administration (FDA) for iron anemia and usedoff-label for MRI. The iron oxide nanoparticles lead to long bloodcirculation with minimal leakage from vasculature, resulting in highvascular delineation and high vascular/tissue contrast.

In some embodiments, the contrast agent is a gadolinium chelate or agadolinium compound. In some embodiments, the gadolinium compound isgadofosveset trisodium (e.g., ABLAVAR®), gadoterate meglumine, gadoxeticacid disodium salt, gadobutrol (e.g., GADOVIST®), gadopenteticdimeglumine, gadobenate dimeglumine, gadodiamide, gadoversetamide, orgadoteridol.

In some embodiments, the contrast agent is introduced in the region ofinterest at a concentration of about 0.1 to 8 mg/kg for humans and 0.1to 15 mg/kg for animals. The concentration can be determined by contrastnecessity and safety for the human, non-human animal, or substance.

Any suitable magnetic resonance imaging (MRI) machine or equipment canbe used. Suitable MRI machines can be found in clinical or hospitalsettings, research laboratories, and the like. In some embodiments, theMRI machine can be capable of generating a static magnetic fieldstrength ranging from about 0.2 T to 14.0 T. In some embodiments, thestatic magnetic field strength can be about 3.0 T or about 7.0 T.

The MRI machine can be set in any suitable manner to operate at a pulsesequence to provide the UTE technique described herein.

The MRI machine can be calibrated as described herein. In someembodiments, the MRI machine is calibrated periodically. In someembodiments, the MRI machine is calibrated monthly, weekly, or daily. Insome embodiments, the MRI machine is calibrated for each new loading ofa subject to be imaged. In some embodiments, the MRI machine iscalibrated using a phantom. In some embodiments, the phantom is a vialcontaining a subject material mixed with a contrast agent. In someembodiments, the subject material is human blood or non-human animalblood.

The MRI machine can provide an image in any suitable manner. In someembodiments, the image can be a three-dimensional representation of aregion of interest. In some embodiments, the image can be a volume of aregion of interest. In some embodiments, the image can be atwo-dimensional representation of a region of interest. In someembodiments, the image can be a slice of a region of interest.

In some embodiments, the response signal is measured along radialtrajectories in k-space. In some embodiments, the response signal ismeasured along orthogonal trajectories in k-space.

In some embodiments, a quantitative contrast-enhanced MRI technique isprovided that utilizes an ultrashort time-to-echo (QUTE-CE) has beenshown to generate positive-contrast images of a contrast agent,particularly using superparamagnetic iron oxide nanoparticles (SPIONs),in vivo. Ultra-fast (e.g. 10-300 μs) signal acquisition has the benefitof producing positive contrast images, instead of dark contrast images,by acquiring signal before tissue magnetization in the transverse planedephases, thus allowing complete T₁ contrast enhancement from SPIONs.Thus, UTE is suited for measuring the concentration from clinicallyrelevant concentrations of FDA-approved ferumoxytol. The techniqueutilizes CA-induced T₁ shortening, combined with rapid signalacquisition at ultra-short TEs, to produce images with little T₂* decay.

Prior art MRI techniques remain semi-quantitative because they areinherently sensitive to extravoxular susceptibility artifacts, fieldinhomogeneity, partial voluming, perivascular effects, and motion/flowartifacts. Imaging techniques that employ a time-to-echo (TE) of half amillisecond or more are particularly susceptible to heterogeneous signalmodifications and are therefore difficult to interpret. Thus, therelationship between MRI signal intensity and CA concentration is widelyrecognized to be complex and nonlinear. Nevertheless, current models forcontrast CA quantification assume a linear relationship between signalintensity and CA concentration or a linear relationship between CAconcentration and relaxivity. Published methods to quantify CAconcentration generally rely on the linear relationship between eithermeasured signal intensity or R₁ relaxation rate and concentration. Therestill remains a high degree of error with this approach in vivo,reported on the order of 15-30%. This high error is due toheterogeneous, non-linear signal changes that are not adequatelydescribed by theory when measuring in vivo. Complex non-linear modelinghas shown limited success (13±9% error in vivo), but is sensitive tosubtle effects from magnetic susceptibility, imperfect B₀ shimming, andchemical shifting. All of these complications become stronger at longerTEs. These complications can be overcome, however, by the presenttechnique, which employs ultrashort TEs.

In some embodiments of the technique here, with an optimized pulsesequence (TE, TR, FA), completely T₁-weighted images can be acquiredwith signal predicted by the SPGR equation as a function ofconcentration. The quantitative nature of QUTE-CE signal has beendemonstrated by accurately measuring the clinically relevantintravascular concentration of Ferumoxytol, an FDA approved iron-oxidenanopharmaceutical, in mice. Indeed, previous techniques that employgadolinium are limited by toxicity and residence time, while othertechniques that employ iron-oxide nanoparticles are limited by negativecontrast, SNR and requirement of high concentration. All previoustechniques are only semi-quantitative, since they require a baseline,produce results based on relative changes, or have too high a degree oferror. However, the QUTE-CE technique provides positive-contrast, highSNR and CNR, since organs are invisible in pre-contrast images at 7.0 T,and a signal completely contingent on intravoxular blood volume andconcentration of contrast agent.

More particularly, the signal in the UTE images is quantitative anddirectly indicative of CA concentration. QUTE-CE can utilize CA-inducedT₁ shortening, combined with rapid signal acquisition at ultra-shortTEs, to negate T₂* decay (>1% signal decay by TE). Under certainapproximations, the UTE signal intensity can be approximated by thespoiled gradient echo (SPGR) equation.

$I = {K\; {\rho \cdot e^{{- {TE}} \cdot {({R_{2_{0}} + {r_{2} \cdot C}})}} \cdot \sin}\; {\theta \cdot \frac{1 - e^{{- {TR}} \cdot {({R_{1_{0}} + {r_{1} \cdot C}})}}}{1 - {{e^{{- {TR}} \cdot {({R_{1_{0}} + {r_{1} \cdot C}})}} \cdot \cos}\; \theta}}}}$

The image intensity in a given voxel measured by QUTE-CE MRI is afunction of both image acquisition and material parameters:

I=f(TE,TR,θ:T ₁ ,T ₂ *:K,ρ)

where TE is the time-to-echo, TR is the repetition time, and θ is theflip angle. TE, TR, and θ are image acquisition parameters defined bythe user. T₁ and T₂* are the longitudinal and transverse relaxationtimes, respectively, that depend on the medium under investigation andthe magnetic field strength applied by the MRI machine. K is a constantthat is determined by the UTE signal intensity as seen by the coil ofthe MRI machine, and ρ is the proton density of the medium. Forultrashort TE values, T₂* effectively equals T₂.

T₁ and T₂ can be written in terms of their reciprocals, relaxation ratesR₁ and R₂, respectively, for the facile determination of relaxivityconstants. For imaging at a single magnetic field strength, the explicitfield dependence is constant and can be omitted. The medium underinvestigation is a desired contrast agent approximately uniformly mixedin blood. Thus, R₁ and R₂ are a function of the initial relaxation rateof the blood (R_(1o) and R_(2o)), the longitudinal and transverseconcentration-dependent relaxivities (r₁ and r₂), and the contrast agentat given concentrations C.

For concentrations in which the relaxation rate is linear,

R ₁ =R _(1o) +r ₁ C  (1)

R ₂ =R _(2o) +r ₂ C  (2)

The UTE signal intensity can be approximated by the spoiled gradientecho (SPGR) equation:

$\begin{matrix}{I = {K\; {\rho \cdot e^{{{- {TE}} \cdot R}\; 2} \cdot \sin}\; {\theta \cdot \frac{1 - e^{{{- {TR}} \cdot R}\; 1}}{1 - {{e^{{{TR} \cdot R}\; 1} \cdot \cos}\; \theta}}}}} & (3) \\{I = {K\; {\rho \cdot e^{{- {TE}} \cdot {({R_{2_{0}} + {r_{2} \cdot C}})}} \cdot \sin}\; {\theta \cdot \frac{1 - e^{{- {TR}} \cdot {({R_{1_{0}} + {r_{1} \cdot C}})}}}{1 - {{e^{{- {TR}} \cdot {({R_{1_{0}} + {r_{1} \cdot C}})}} \cdot \cos}\; \theta}}}}} & (4)\end{matrix}$

Once the relaxivity constants have been obtained, the image acquisitionparameters have been established, and Kρ has been calibrated, unknown CAconcentrations can be quantified experimentally using Equation 4. Thus,after calibration and having knowledge of relaxation constants of bloodR_(1o), r₁, R_(2o) and r₂, a vascular region of interest can be scannedin vivo or in vitro to produce quantitative images.

One embodiment of a procedure is described with reference to FIG. 2.

In step 1, calibration phantoms containing blood (1% heparin) are dopedwith clinically relevant concentrations of ferumoxytol (0-150 μg/mL).

In step 2, for each calibration sample, T₁ and T₂ are measured, fromwhich the relaxivity constants R_(1o), r₁, R_(2o), and r₂ can beextrapolated. See FIG. 3. In FIG. 3, R₁ and R₂ (1/T₁, 1/T₂) are plottedas a function of concentration. Relaxation rate constants can be takenfrom the fitted lines.

In step 3, a UTE protocol is established with optimized TE, TR, and θimage acquisition parameters and a fixed trajectory, precalculated witha symmetric phantom, described below.

In step 4, K is measured together with ρ and Kρ, assuming the protondensity of whole blood is constant, and serves as a calibration for thegiven UTE protocol.

In step 5, positive-contrast images using the optimized parameters areacquired in vivo.

In step 6, CA concentrations in each voxel are calculated directly fromUTE signal intensity, by application of the SPGR equation (Equation 4).

Unlike the four relaxivity constants in Equation (4), which only need tobe measured for each magnetic field strength, Kρ is a constant thatneeds to be determined for each imaging protocol, as it depends onacquisition parameters (TE, TR, θ, matrix size) and coil hardware of theMRI machine to be used. Thereafter, Kρ can be used for all subsequentscans. Calibrating Kρ can be executed as follows:

1. Phantoms of blood doped with the desired contrast agent are preparedat known concentrations.

2. A UTE protocol with specific determined acquisition parameters isperformed using the prepared phantoms.

3. Regions of interest are drawn on the images inside the vials in thecenter Z-axis axial slice of the three-dimensional (3D) image to obtaina mean intensity and standard deviation.

4. The intensity is used in conjunction with the SPGR equation todetermine Kρ (TE, TR, θ, C are known parameters, and relaxivityconstants can be measured, as described herein).

5. The average value of Kρ is taken as a calibration constant.

Once this procedure is completed, Kρ can be used for all subsequentquantitative calculations using this protocol.

Because the acquired signal is quantitative, the technique can beapplied to other applications, in particular, partial blood volumemeasurements using two volume methods, and identifying accumulatednanoparticles, such as superparamagnetic iron oxide nanoparticles(SPIONs). Thus, in some embodiments, this technique can be used forapplications such as tumor vascular imaging and subsequent nanoparticleaccumulation therein. In some embodiments, the technique can be used toprobe the brain in an attempt to obtain a quantitative biomarker forvascularity. In some embodiments, the technique can be used fordiagnostic functional imaging and image-guided drug delivery with anappropriate contrast agent.

For example, enhanced permeability and retention (EPR) describes thepropensity of some tumors to passively accumulate nanoparticles.Although the EPR effect holds promise for increased delivery ofchemotherapeutics to tumors, it is difficult to assess whether or notnanoparticle chemotherapy will result in significantly greater benefitsthan a standard chemotherapeutic treatment. It is difficult to predictthe amount of EPR both between patients and between metastatic tumors inthe same patient. Superparamagnetic iron-oxide nanoparticles (SPIONs)have been employed as surrogates for predicting secondary nanoparticleaccumulation in clinical trials, but imaging performed with negativecontrast suffers from poor discrimination of nanoparticle accumulationin heterogeneous tissue (see, for example, FIG. 15, middle column). Thepresent technique of QUTE-CE MRI can render SPION accumulation withunambiguously delineable positive contrast (FIG. 15, right column), andcan distinguish between necrotic and well vascularized tumor tissue as apossible biomarker for predictive accumulation in a subcutaneous tumor.

In some embodiments, the technique can be used to determine blood volumefractions. In some embodiments, a partial blood volume of a region ofinterest can be determined. In some embodiments, a cerebral blood volumefraction can be determined.

More particularly, T₂- and T₂*-weighted images are sensitive toperivascular effects, extravoxular susceptibility artifacts, and flowartifacts. However, in UTE the signal is restricted to effects thatoccur intravoxularly and flow effects are completely suppressed bynon-slice selective RF pulses. Thus, the measured signal from any givenvoxel is given by a combination of intensity from the fraction of thevolume occupied by tissue, f_(T), and fraction occupied by CA-dopedblood, f_(B)

I _(measured) =f _(B) I _(B) +f _(T) I _(T)  (5)

where I_(T) is the tissue intensity, I_(B) is the blood intensity andI_(M) is the total measured intensity. This equation makes an implicitassumption that only blood and tissue are present in each voxel and thatthe tissue itself is approximately homogeneous within a single voxel. Itfollows from this base assumption that, f_(T)=1−f_(B). Thus, if bloodand tissue intensities (I_(B) and I_(T)) are known, then f_(B) can bemeasured directly from any scan as simply,

$\begin{matrix}{f_{B} = \frac{I_{M} - I_{T}}{I_{B} - I_{T}}} & (6)\end{matrix}$

However, if these intensity values are not known then it is necessary toperform at least two scans. By performing both a pre-contrast andpost-contrast scan, two measurements per-voxel, I_(M) and I_(M)′respectively, can be made. Then changes in the measured intensity can beassessed using Equation 5,

Δ1_(M) =f _(B) ′I _(B) ′−f _(B) I _(B) +f _(T) ′,I _(T) ′−f _(T) I_(T)  (7)

where all primes denote values in the post-contrast injection scan.Provided that the subject is in the same neurological state, it can beassumed that, f_(B)′=f_(B) and f_(T)′=f_(T). Further, assuming thecontrast agent is entirely confined to the vasculature then on aper-voxel basis, I_(T)′≈I_(T). Using these assumptions, f_(B) can besolved for, such that,

$\begin{matrix}{f_{B} = \frac{I_{M}^{\prime} - I_{M}}{I_{B}^{\prime} - I_{B}}} & (8)\end{matrix}$

This equation is sufficient for calculating the blood fraction given apre-contrast scan of a subject in the same or substantially the samefunctional state. This is adequate, for example, for a quantitativecerebral blood volume atlas of a subject animal, since the subjectanimal can be anesthetized pre- and post-contrast. To determinefunctional CBV information when the CBV is assumed to be changing, then,using Equation 7 and assuming that I_(T)′≈I_(T) without assuming thesame initial fraction of blood, the equation becomes,

$\begin{matrix}{f_{B}^{\prime \;} = \frac{I_{M}^{\prime} - I_{M} + {f_{B}\left( {I_{B} - I_{T}} \right)}}{I_{B}^{\prime} - I_{B}}} & (9)\end{matrix}$

Here, f_(B) is the blood fraction if the precontrast image utilized forI_(M) is in the precontrast state. Through the application of thisequation, CBV can be determined in scans for which CBV is assumed tohave changed between pre- and post-contrast. In some embodiments, thisequation can be used between an anesthetized pre-contrast scan and thenon-anesthetized post-contrast scan of a subject animal.

By utilizing the QUTE-CE technique, the physical problems of acquiringsignal late after excitation can be addressed: measurements are madewith negligible blood displacement and extravoxular susceptibility andsignal dephasing is eliminated at low TEs. Inter-TR flow effects can besuppressed by using a broad suppression pulse, which producesT₁-weighted positive contrast images with signal intensity per voxelproportional to the amount of contrast-agent doped blood, or CBV.Additionally, these measurements can be completely insensitive to bloodoxygenation and the contrast agent concentration can be in theclinically appropriate range. These results clearly demonstrate thecapability of the present technique QUTE-CE to measure absolute CBV withsufficient accuracy to enable an advantageous approach to functionalMRI.

In some embodiments, the technique provides an enhanced signal to noiseratio (SNR) and/or an enhanced contrast to noise ratio (CNR). The SNR isdefined as the average signal from an ROI drawn in the media divided bythe standard deviation of the noise determined by an ROI located outsidethe sample in air. In some embodiments, a difference in SNRs of doped-and undoped-media can be used to determine CNR in vitro. In someembodiments, the CNR can be computed by subtracting the SNR of a regioncontaining primarily tissue from the blood SNR. A time-adjusted SNR andCNR take into account the duration of a scan by dividing by √{squareroot over (TR)}, which normalizes SNR and CNR by the duration of thescan. In some embodiments a contrast efficiency can be determined, asfollows:

$\begin{matrix}{{{Contrast}\mspace{14mu} {Efficiency}} = {\frac{CNR}{\sqrt{{scan}\mspace{14mu} {time}}}*\frac{{Subset}\mspace{14mu} {of}\mspace{14mu} {volume}\mspace{14mu} {imaged}}{{Total}\mspace{14mu} {volume}}}} & (10)\end{matrix}$

EXAMPLES Example 1

In one example, a contrast-enhanced, 3D UTE technique was used forcardiac and thoracic angiography imaging in mice. Contrast-enhanced 3DUTE imaging with ferumoxytol produced images in which pre-contrast mostorgans are completely invisible (FIG. 4(a),(c)) as facilitated by anon-slice selective pulse at a low TR, which suppressed most of thesignal from water protons in mice. Pre-contrast signal from bloodentering the periphery of the image space into the stomach is apparentbecause of incoming water protons with fresh longitudinal magnetizationas compared to those that had already been saturated. Post-contrastimages rendered high CNR images of all the vasculature in whichnanoparticle iron circulated (FIG. 4 (b),(d). Thus, an ultra-short TEallowed for completely T₁-weighted, snapshot images of CA distributed invivo.

1.1 One-Hundred UTE Experiments Reveal an ‘Optimal Zone’ at 7 T

The ability to predict CA concentrations from UTE intensity using theSPGR equation is influenced by image acquisition parameters TE, TR, andθ. A 3D UTE radial k-space sequence, readily available from the Brukertoolbox, was selected and an imaging protocol was established a with FOV(3×3×3 cm³), matrix mesh size (128×128×128), and 51,360 radials, whichrendered 234 μm x-y-z resolution images with a 3 m scan time for TR=3.5ms. The image reconstruction trajectory was fixed using a 5 mM coppersulfate (CuSO₄) phantom constructed from a 50-ml centrifuge tube.Experiments were performed on whole calf and mouse blood (1% heparin)doped with ferumoxytol (0-250 μg/ml). A high bandwidth (BW)radiofrequency (RF) pulse was used to avoid complications for cases inwhich a low BW compared to T₂* may cause a curved trajectory for themagnetization vector M_(z) out of the z-plane. Assuming T₂*≈T₂ atultra-short TE values, the 200 kHz BW yielded ultrafast excitationcompared to the lowest T₂ value of 5.5 ms at 150 μg/ml. All experimentsperformed on acquisition parameters optimization were performed with a72 mm Bruker quad coil.

For calf blood, 100 scans were executed covering combinations of 5 TEs(13, 30, 60, 90, and 120 μs), 5 TRs (3.5, 5, 7, 9, and 11 ms) and 4 θs(10, 15, 20, and 25°). Six 2-ml phantoms of ferumoxytol-doped calf bloodat (0-250 μg/ml ferumoxytol) were arranged in pentagonal fashion withthe 0 μg/ml vial at the center inside of a 72-mm Bruker quad coil. Kρwas calibrated per image, with the 0 concentration exceptionallyexcluded in calculations because the noise from surrounding highconcentrations rendered a poor measurement. It was found that higherconcentration UTE signals deviated from the SPGR equation, owing to thenon-linear behavior of the relaxation rate at high concentrations; thusonly θ, 50, 100 and 150 μg/ml phantoms were considered in the analysisin FIG. 5. The results are thus relevant for clinical concentrations offerumoxytol, considering 100 μg/ml is roughly equivalent to a singlei.v. bolus of 510 mg in adult humans. Accuracy was observed to be moststable at TE=13 μs, TR=3.5 ms, and θ=20° (FIG. 5(a)). In this ‘optimalzone’, the average in vitro error between QUTE-CE measurements and knownferumoxytol concentrations was less than 4 μg/ml, but increasedsignificantly as TR and θ deviated (FIG. 5(b)). However, changes in TEup to 120 μs had little impact on concentration measurements (FIG.5(c)). This information is used for obtaining precise concentrationmeasurements from theory. The agreement between the measured signalintensity and the SPGR equation for known concentrations at theoptimized parameters was excellent, as shown in FIG. 5(d). The absolutevalues for SNR and CNR at 150 μg/ml ferumoxytol in the optimal zone were72 and 57 respectively (FIG. 5(e)).

The SNR was defined as the average signal from an ROI drawn in the mediadivided by the standard deviation of the noise determined by an ROIlocated outside the sample in air. ROIs for these measurements weredrawn in the center z-slice of the phantom tubes. A difference in SNRsof doped- and undoped-media were used to determine CNR in vitro. Thetime-adjusted SNR and CNR take into account the duration of the scan bydividing by √{square root over (T)}R, which normalizes SNR and CNR bythe duration of the scan. The time-corrected SNR and CNR also tended tobe higher in the optimal zone (FIG. 6). Relaxation rate measurementswere repeated after the experiment to ensure that no blood coagulationwas present (FIG. 3). These results validate the use of the SPGREquation 4 to determine unknown concentrations.

To ensure validity of phantom measurements, experiments were repeatedwith mouse blood with 5 TE values (14, 30, 60, 90, and 120 μs) and 5 TRvalues (4, 5, 7, 9, and 11 ms) at θ =20°. Six 2-ml vials of ferumoxytol(50, 75, 100, 125, 150 and 175 μg/ml) were arranged around a center vialof 5 mM copper sulfate (CuSO₄). The same pattern for the optimal zonewas confirmed in mouse blood, with absolute concentration errors similarto the previous experiment.

1.2 QUTE-CE Calibration and Validation

To establish the UTE protocol, the following parameters were fixed: FOV(3×3×3 cm³), matrix mesh size (200×200×200), TE (13 μs), TR (4 ms), andθ (20°). TR was slightly higher than the optimal value because ofhardware and memory constraints. A 50-ml cylindrical phantom filled with5 mM CuSO₄ was analyzed to fix a reconstruction trajectory.

Phantoms (0-150 μg/ml ferumoxytol) were placed one at a time forcalibration of Kρ to produce ideal images with low noise (FIG. 8(a)-(d),FIG. 9(a)). This protocol and calibration was used for all subsequent invitro and in vivo experiments. The coil used for in vitro and in vivomeasurements is a 30 mm 300 MHz Mouse MRI coil (Animal Imaging Research,LLC, Holden, Mass.).

To assess in vitro performance of QUTE-CE, doped phantoms were createdby serial dilution of ferumoxytol from 128 and 96 μg/ml (FIG. 8(e)-(i)).3D UTE was performed and concentrations were calculated voxel by voxelfor images containing multiple phantoms (FIG. 8(b)).

A linear correlation (R²=0.998) was observed between the measured andknown ferumoxytol concentrations (FIG. 9(b)). The average residual errorin measured concentration was found to be 2.57±1.34 μg/ml, or 3.04% forsamples between 48-128 μg/ml (FIG. 9(b), insert). Measurements weretaken at the center of the z-axis in the imaging space, after convertingfrom UTE intensity to concentration (FIG. 9(c),(d)), to minimizeinhomogeneous effects from imperfect transmit field (B₁ ⁺) homogeneity.The effect of B₁ inhomogeneity on concentration measurements wasassessed as a function of distance deviated from the center z-axis alongthe tubular phantoms as far as possible in the 3D images (FIG. 9(e);FIG. 10). B₁ ⁺ inhomogeneity was most significant for the highestconcentrations, adding about 10% error to the 128 μg/ml phantom at adistance of 50 mm.

1.3 Quantification of Blood Pool Ferumoxytol In Vivo

All animal experiments were conducted in accordance with theNortheastern University Division of Laboratory Animal Medicine andInstitutional Animal Care and Use Committee. QUTE-CE was used to measurethe concentration of ferumoxytol in the blood of mice using the sameimaging protocol, coil, trajectory measurement and calibration for invitro measurements in the QUTE-CE calibration and validation discussedabove. Ferumoxytol is approved for an intravenous injection of 510 mg inhumans. Assuming an average adult blood volume of 5 L, a single bolus offerumoxytol is expected to produce initial blood concentration of about100 μg/ml. To remain clinically relevant in the selection ofconcentrations, starting blood concentrations of 100-200 μg/ml in micewas aimed for.

Healthy anesthetized Swiss Webster mice (n=5) received a one-time i.v.bolus injection of 0.4-0.8 mg ferumoxytol for a starting blood poolconcentration of 100-200 μg/ml (diluted to 4 mg/ml in PBS) and wereimaged longitudinally after injection (0 h, 2 h and 4 h). Pre-contrastimages were also acquired. Given the assumption that blood in mice isabout 7% of body weight, for a 50 gr mouse an initial yield of 115-230μg/ml was predicted. This is similar to clinical concentrations where aninjection of 510 mg produces a blood concentration of about 100 μg/mlfor a total blood volume in the average adult human of 5 L.

A single UTE protocol was used for all images. To establish the UTEprotocol, the following parameters were fixed (as above for QUTE-CEcalibration and validation): FOV (3×3×3 cm³), matrix mesh size(200×200×200), TE (13 μs), TR (4 ms), and θ (20°). TR was slightlyhigher than the optimal value because of hardware and memoryconstraints. A 50-ml cylindrical phantom filled with 5 mM CuSO₄ wasanalyzed to determine the k-space trajectories for image reconstruction.

Reconstructed 3D intensity image data was re-scaled back to the originalintensity measurement (as necessary with Bruker file format files, onemust divide by the receiver gain and multiply by scaling factor calledSLOPE). Intensity data was then converted to concentration via theoryusing a custom MATLAB script to solve numerically the nonlinear SPGRintensity using Equation 4.

Mice were imaged longitudinally after injection (0 h, 2 h and 4 h). Eachimaging session was followed by a submandibular bleed (200 μl) to obtainblood for elemental iron analysis. Pre-contrast images were alsoacquired. Comparison of the pre-contrast (FIG. 11(a)) and post-contrast(FIG. 11(b)) images showed positive-contrast enhancement, facilitatingclear delineation of the mouse vasculature with a comparable SNR(23.2-49.4) and CNR (4.0-41.5) to similar ferumoxytol concentrations invitro. The CNR was computed by subtracting the SNR of a regioncontaining primarily tissue from the blood SNR. 3-D segmentation with3DSlicer, centered the measured mean concentration ±2.5 standarddeviations, allowed reconstruction of numerous vessels (FIG. 11(c)).Auto-segmentation and 3D rendering was performed using 3DSlicerutilizing established modules. First, all voxels within the range of themean concentration ±2.5 standard deviations as measured in the leftventricle were selected (using the ThresholdEffect module). The GrowCutalgorithm module was then used to separate out the vasculature from therest of the image. The ChangeLabel Effect module was used to uniquelyselect the vasculature segment, for which a model was created with theModel Maker module.

To quantify the blood pool ferumoxytol concentration, blood draws wereperformed after each imaging session and quantified by inductivelycoupled plasma atomic emission spectroscopy (ICP-AES) analysis (FIG.11(d); FIG. 12). QUTE-CE proved to be highly accurate, with an averageof 7.07% (6.01±4.93 μg/ml) error across all 15 measurements(concentrations 30-160 μg/ml). The maximum observed residual error invivo was 13.50 μg/ml, compared to 5.0 μg/ml in vitro (FIG. 11(d),inset). The linear correlation coefficient between ICP-AES and QUTE-CEmeasurements was R²=0.954. The QUTE-CE ROI for quantification wasroutinely drawn in left ventricle throughout several slices (FIG. 11(d)insert) and analyzed in a blinded manner. Almost all ROIs were within 5mm of the center of the z-axis, which minimized error from B₁ ⁺inhomogeneity; thus there was no correlation between error and distanceto the center of the z-axis. Longitudinal measurements of ferumoxytolconcentration in vivo showed a clear, reproducible decay in blood poolconcentration, making it possible to measure the half-life of thecontrast agent from images alone. The ferumoxytol half-life was found tobe 3.92±0.45 hours, with an average R²=0.988 across 5 mice (FIG. 11(e)).

1.4 Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES)

ICP-AES was performed to analyze the iron-oxide nanoparticle (IONP)content in doped whole animal blood. Briefly, preparation of IONP-dopedmedia involved the full digestion of the sample in a Milestone EthosPlus Microwave. Full digestion was achieved by taking 0.1 ml of sampleand adding 6 ml of concentrated nitric acid, 2 ml of hydrogen peroxideand 2 ml of pure water, and running a protocol on the microwave thatramped the temperature up to 210° C. for 15 minutes. Followingdigestion, the samples were dried, resuspended in 5 ml of 2% nitricacid, and measured using ICP-AES. A standard curve utilizing amono-elemental iron was run to ensure high instrument fidelity(r²=1.000). Each data set (n=5) was fitted with the pooled slope andaverage intercept (n=3 per set) to account for offsets in baseline ironcontent, for a total of n=15 in vivo measurements.

1.5 Conclusions on Vascular SPION Concentration Measurements

By choosing optimized image acquisition parameters to minimize the errorin concentration, including an ultra-short TE, the SPGR equation couldbe used to accurately measure ferumoxytol concentrations in vitro and invivo. This optimized UTE protocol allows signals to be acquiredmicroseconds after excitation, before cross-talk between voxels canoccur, thereby eliminating both extra-voxular susceptibility and floweffects. Indeed, the average blood flow velocity in mice is ˜10-100 mm/s(excluding the largest arteries), and thus blood displacement is twoorders of magnitude less than the voxel size during image acquisition. Alow TR suppresses flow effects for concentration quantification as wellas suppressing pre-contrast tissue signal, rendering high SNR and CNRratios similar to those observed in vitro. This optimization of the UTEprotocol yields a strong correlation between the theory and experimentalmeasurements, allowing the QUTE-CE image contrast to be quantified with2-4× more precision than other reported techniques.

Longitudinal QUTE-CE measurements can be used to determinepharmacokinetic parameters. The ability to distinguish time-dependentchanges in blood pool ferumoxytol concentration was demonstrated with aprecision of about 0.1 mM at 7 T up to about 3 mM for the estimation ofCA half-life. These measurements were independently validated ex vivousing ICP-AES. The ferumoxytol half-life measured in mice by QUTE-CE(3.92±0.45 hr) is comparable to that measured by others usingradiolabelled ferumoxytol in rats (3.9 hr) and rabbits (4.4 hr). QUTE-CEconcentration measurements are extrapolated directly from UTE signalintensities, without pharmacokinetic modeling or image registration. Assuch, no assumptions about tissue structure or function, orheterogeneities contained therein, are required for concentrationanalyses. This ability to longitudinally quantify blood pool CAconcentration is an advantage of the QUTE-CE technique.

In summary, the technique described here allows clinically relevantconcentrations of ferumoxytol to be measured non-invasively andquantitatively with high precision. QUTE-CE data shows excellentagreement with theory with image acquisition parameters optimized toreduce error. The robustness of this technique is based on the use of anultra-short TE, which allows the SPGR equation to be applied.Longitudinal measurements of blood pool ferumoxytol can be acquired invivo with high precision for estimation of ferumoxytol half-life. Thisability to longitudinally quantify blood pool CA concentration is anadvantage of the QUTE-CE method, and makes MRI competitive with nuclearimaging.

Example 2

In one example, the technique was applied to demonstrate measurement ofnanoparticle accumulation in tumors in mice.

2.1 Methods

All animal experiments were conducted in accordance with theNortheastern University Division of Laboratory Animal Medicine andInstitutional Animal Care and Use Committee. MRI images were obtained atambient temperature (˜25° C.) using a Bruker Biospec 7.0 T/20-cm USRhorizontal magnet (Bruker, Billerica, Mass., USA) equipped with a20-G/cm magnetic field gradient insert (ID=12 cm, Bruker) and the samequadrature 300 MHz, 30 mm Mouse MRI coil was used for all in vivo workas previously utilized for mouse experiments above in Example 1 (AnimalImaging Research, LLC, Holden, Mass., USA).

PC 3 cells were injected into the right flank of immunocompromisedFoxNui mice (n=5, Charles River Laboratories). After tumors reachedabout 0.5-1.0 cm³, animals underwent three separate imaging sessions:Session 1—pre-contrast T₁, T₂ and QUTE-CE measurements, Session2—immediate post-contrast QUTE-CE measurement and Session 3-24 hpost-contrast T₁, T₂ and QUTE-CE measurements. For contrast, 100 μl offerumoxytol diluted to 6 mg/ml was injected i.v. to render a bloodconcentration of ˜200 μg/ml Fe (2× clinical dose).

T₁ and T₂ measurements were made with the Bruker RAREVTR and MSMEsequences respectively, similar to the characterization study inExample 1. Tumors 1-4 had slightly different scan T₁ and T₂ protocolsthan tumor 5. Protocol for scans 1-4 was the following: RAREVTRs of [600800 1200 1800 4000] ms were used to make the fits for T₁ with TE=7.37ms, averages=2, FOV=0.3×0.3 cm², matrix size=100×100, 50 slices with 0.3cm thickness with no slice overlap, and total scan time of 28 m 0 s. ForMSME, echos were at [10 20 30 40 50 60 70 80 90 100] ms, TR=6000,averages=2, FOV=0.3×0.3 cm², matrix size=100×100, 50 slices with 0.3 cmthickness and no slice overlap with a total scan time of 20 m 0 s. Fortumor 5 the protocols were: RAREVTRs of [600 800 1200 1800 4000] ms wereused to make the fits for T₁ with TE=7.37 ms, averages=2, FOV=0.3×0.3cm², matrix size=100×100, 50 slices with 0.3 cm thickness and a negativeslice-gap of −0.1 mm to reduce noise with a total scan time of 19 m 33s. For MSME, echos were at [10 20 30 40 50 60 70 80 90 100] ms, TR=6000,averages=2, FOV=0.3×0.3 cm², matrix size=100×100, 50 slices with 0.3 cmthickness and a negative slice-gap of −0.1 mm to reduce noise with atotal scan time of 20 m 0 s. The same 3D UTE protocol was used as inExample 1, with TE=13 μs, TR=4 ms, FA=20°, isotropic FOV=0.3 mm³ andmatrix=200³, with a total scan time of 13 m 56 s. Every attempt was madeto produce high-quality images that could be compared to QUTE-CE data.

2.2 QUTE-CE Rendered Unambiguous Contrast of SPIONs in Tumors

Contrary to more standard MRI techniques, QUTE-CE pre-contrast imagesrendered a nearly homogenous signal with a Gaussian distribution in thetumor (FIG. 13(a),(d)). The immediate post-contrast images rendered thevasculature clearly (FIG. 13(b),(e)) and skewed the distribution ofvoxels within the whole tumor to the left, and however also increasedthe overall mean of the signal intensity because the movement of voxelswithin the tumor is to the right, leaving a long bright tail with thebrightest voxels represented by those containing 100% blood. 24 h afterthe initial administration of ferumoxytol the vasculature was no longervisible, but the locations within the tumor that had passivelyaccumulated SPIONs resulting from the EPR effect becomes apparent (FIG.13(c),(f)). While the distribution of voxels within the tumor becameless skewed, the overall shape was still slightly skewed to the left andthe mean of the distribution had moved to the right. Nanoparticleaccumulation in the post-contrast image was heterogeneous andunambiguous.

2.3 Angiography and TBV in Tumors

Assuming a partial 2-volume model of blood and tissue (as discussedfurther below with regard to rat brain imaging), it is possiblecalculate the tumor blood volume (TBV). In this example, this wasperformed using Equation 5.4, taking an average value for thepre-contrast intensity (instead of voxel by voxel subtraction), sincethe overall distribution had been shown to be Gaussian, and for the samereason assuming that the pre-contrast blood value was indistinguishablefrom the pre-contrast tissue intensity, setting them equal. While theseapproximations are apparently valid given the distribution ofpre-contrast signal intensity, it is also noted that for a more completemeasurement one would not only have to have an accurate registration ofpre- and post-contrast images, but also have measurements of the B₀ andB₁ fields to remove effects of signal inhomogeneity. The resultantapproximation for CBV is shown in FIG. 14, in which a clear range of TBVvalues are apparent, delineating areas of the tissue with high contrastin regard to overall vascular health

2.4 Comparisons of QUTE-CE Contrast to Standard Techniques

The standard prior art technique to quantify SPION accumulation is totake T₂ measurements pre- and post-contrast and visualize accumulationvia a subtraction image. It is less likely that T₁-subtraction would beperformed in the prior art, because of the very low r₁/r₂ ratio, whichgreatly favors rendering T₂- or T₂*-weighted imaging. In contrast,because QUTE-CE is purely T₁-weighted, images in FIG. 15 compare CNRimages for QUTE-CE, ΔT₁ and ΔT₂. Registration for these images wasperformed by manually coloring the tumor in 3DSlicer in pre- andpost-contrast images, then using the co-registration tool in the SPM12Toolbox in MATLAB with nearest-neighbors interpolation to maintain theintegrity of the quantitative values.

The heterogeneity of the tumor for T₂ contrast necessitated apost-contrast imaging session to delineate particle accumulation (FIG.15 top, center column), whereas the relatively homogenous signalthroughout the tumor with QUTE-CE rendered similar information in thepost-contrast image as in the difference image (FIG. 15 top, rightcolumn). Accordingly, QUTE-CE imaging was beneficial over prior artT₂-weighted or T₁-weighted imaging in that there was no need for apre-contrast imaging session or subsequent co-registration, which can becumbersome in routine practice. Nevertheless, CNR measurements in ROIswere made to quantitatively compare the efficacy of the separatetechniques at producing contrast. The ROIs were drawn independently onthe ΔT₂ and QUTE-CE difference images and the common ROI was chosen foranalysis in order to produce fair results. ROI location and tumorreference images can be seen in FIG. 16 and subsequent CNRs are in FIG.17 and Table 1.

CNR from QUTE-CE is measurably superior in 3 out of 5 of the PC3 tumorROIs (FIG. 17), and T₂ contrast is superior for the other two. CNR forQUTE-CE images was calculated as a simple difference in SNR, whereas forT₁ and T₂ the formula was a difference in relaxation time divided by thepropagated error. Zero voxels were not included (neither voxels outsideof a modality's imaging space nor from T₂ or T₁ measurement holes frombad fits) in the ROIs for comparison. Whether or not one techniquesurpassed the other depended on the heterogeneity and initial T₂ valueof the tumor. For example, Tumor 5 (FIG. 16) had a bright T₂ valueprecisely at the spot where, for this tumor, contrast accumulation waslocalized. Thus, the effect on ΔT₂ was very strong for this tumor, butnot so pronounced for QUTE-CE. On the other hand, Tumor 1 had a greatdeal of accumulation and nominal contrast was 2× superior with QUTE-CE.Overall this shows that QUTE-CE imaging is at least on par for CNRcompare with T₂.

In addition to these measurements, the contrast efficiency was alsocalculated (Table 1), using Equation 10 above. The total volume spacewas taken as 3×3×3 cm³, or 27 cm³, and the subset of that volume perscan was spherical for QUTE-CE with a 3 cm diameter and Cartesian for T₁and T₂ images with 3×3×1.5 cm³ space for Tumors 1-4, and 3×3×1.5 cm³ forTumor 5. Over the 5 tumors, QUTE-CE outperformed T₂ imaging in terms ofcontrast efficiency by 1.02±0.44 vs. 0.98±0.41.

TABLE 1 CNR and contrast efficiency PC3 tumor ROIs CNR (n = 5 tumors) T1std T2 std QUTE-CE std Tumor 1 2.45 0.60 5.29 1.10 9.16 2.52 Tumor 22.21 0.67 4.10 1.61 4.99 1.81 Tumor 3 0.80 0.60 2.02 4.06 2.64 2.12Tumor 4 1.81 0.57 6.35 2.39 4.08 1.99 Tumor 5 1.90 0.79 9.45 2.11 2.521.63 Average 1.83 0.65 5.44 2.25 4.68 2.01 Contrast Efficiency * 100 (n= 5 tumors) T1 std T2 std QUTE-CE std Tumor 1 0.39 0.09 0.99 0.20 2.000.55 Tumor 2 0.35 0.11 0.76 0.30 1.09 0.39 Tumor 3 0.13 0.09 0.38 0.760.58 0.46 Tumor 4 0.28 0.09 1.18 0.44 0.89 0.43 Tumor 5 0.27 0.11 1.600.36 0.55 0.36 Average 0.28 0.10 0.98 0.41 1.02 0.44

Thus, delineating SPION accumulation using QUTE-CE was advantageouscompared to ΔT₂ or ΔT₁ imaging, in that the post-contrast image containssufficient information for nanoparticle localization, eliminating theneed for pre-contrast images (FIG. 15). QUTE-CE CNR was comparable to T₂CNR, and was superior for 3/5 tumor.

2.5 Conclusions on Tumor Imaging with QUTE-CE

An advantage of delineating SPION accumulation using QUTE-CE, comparedto ΔT₂ or ΔT₁ imaging, is that the post-contrast image containssufficient information for nanoparticle localization, eliminating theneed for pre-contrast images (FIG. 15). QUTE-CE CNR was comparable to T₂CNR, and was superior for 3/5 tumor. Quantification of CA accumulationin tissues is of great clinical interest.

Example 3

In one example, the technique was applied to accurately measure CAconcentration in the blood of mice as well as provide a new angiogram,measuring absolute quantities of CBV on a voxel-by-voxel basis. Aquantitative blood volume atlas of the rat brain was developed, both interms of absolute CBV and capillary blood volume, demonstrating that thetechnique can be utilized for quantitative steady-state functionalimaging by measuring changes in CBV in the rats induced by a 5%CO₂-challenge and anesthesia by 3% isoflurane.

3.1 Methods

All animal experiments were conducted in accordance with theNortheastern University Division of Laboratory Animal Medicine andInstitutional Animal Care and Use Committee. MRI images were obtained atambient temperature (˜25° C.) using a Bruker Biospec 7.0 T/20-cm USRhorizontal magnet (Bruker, Billerica, Mass., USA) equipped with a20-G/cm magnetic field gradient insert (ID=12 cm, Bruker). Healthyanesthetized Sprague Dawley (SD) rats (n=12), average weight 300 g, werefitted with an i.v. tail vein catheter capped with heparinized saline.SD rats are widely used to study varying neuropathies. They are also ageneralized strain of lab rat. For these reasons, SD rats were chosenfor this study, and provide an avenue for future comparison for studiesinvolving neuropathy. Rats were subsequently placed into a custom ratimaging apparatus capable of awake-animal imaging. Since the animals areawake for part of the imaging session, the animals were first habituatedto the imaging process and restraint apparatus over a period of 4-5days.

The imaging experiment included one pre-contrast anesthetized scan andthree post-contrast 3DUTE scans taken with optimized parameters: FOV(3×3×3 cm³), matrix mesh size (200×200×200), TE=13 μs, TR=4 ms, andθ=20°. For contrast, a bolus injection of 0.7 ml of ferumoxytol dilutedto 6 mg/ml was injected after the pre-contrast scan to get a bloodconcentration of about 200 μg/ml Fe (2× clinical approval). Followingcontrast injection, three scans were taken to assess the various statesafter leaving the animal 15 minutes to awaken completely fromanesthesia. First, 5% CO₂ was delivered to the rat and after 1-2 minutesof this condition the scan was initiated. Next, the 5% CO₂ gas wasreplaced with air at the same flow-rate, and after 1-2 minutes the scanwas initiated. Third, 3% isoflurane gas replaced the air, and after 1-2minutes of this condition the scan was initiated. Isoflurane percent wasreduced in the case of respiration becoming lower than about 20-30breaths/minute.

3.2 Cerebral Angiographic Imaging in Rats

QUTE-CE produced MRAs with quantitative signal measurements invasculature. FIG. 18 illustrates bright positive contrast using SPIONswith TOF effects limited to the periphery (FIG. 18(a)) and notencountered in the brain (FIG. 18(b)). The pre-contrast images of thebrain and head were nearly invisible pre-contrast, but after i.v.administration of a clinically relevant dose of ferumoxytol the bloodbecame bright. This is atypical in MRI, and more like nuclear imagingtechniques, where image contrast is solely dependent on CA location andconcentration.

This technique differs from TOF and PC imaging. From FIG. 19, it can beseen that QUTE-CE MRA was not sensitive to vessel orientation, unlikeTOF-based imaging schemes. Glands and other organs are visibleeverywhere in the rat head, contingent only on the amount of blood (orcontrast) per voxel. This technique differs from DWI and QSM. With thistechnique, delineation of vessels neglects blood flow, as was also shownin the mouse heart (Example 1 above), and the signal itself isquantitative. In addition, GBCAs are employed in typical CE-MRA, whichare not strictly limited to the vascular area, whereas thenanoformulation of the SPION ferumoxytol is. Thus, it is possible toutilize the quantitative aspects of the signal to not only determine thecontrast agent concentration in the vasculature, but also to determinequantitative information about the state and function of tissuevascularity. While MRA and fMRI methods have proven useful for measuringsemi-quantitative and relative CBV based on percent changes in anarbitrary MR signal, the absolute resting state CBV can be indicative ofthe overall health, as it is well established that many neuropathiesresult in vascular abnormalities.

3.3 Signal Inhomogeneity and Quantitative Measurements

The homogeneity profile of B₀ and B₁ ^(+/−) was also accounted for bynoting the physical design of the excitation/recording coil of the MRIequipment. A rat-brain 300 MHz, 30 mm diameter (Animal Imaging Research,LLC, Holden, Mass., USA) quadrature coil was used for all measurements.Quadrature coils have the added benefit of more efficiently exciting andmeasuring the circularly polarized spins, with an overall gain of√{square root over (2)}SNR. Both channels were assumed to operate withminimal coupling and each was subject to thermal noise which was assumedto induce standard Gaussian distributions in their recordings. Thesignals actually received by these individual channels in frequency (orwave-number) space were then Fourier transformed into position space andcombined into a single magnitude image with the aforementionedintensity, I_(M). To denote the fact that these channels were orthogonaland because the Fourier transform does not affect complexity (in thesense of complex numbers), one channel was labeled “real” and the other“imaginary.” Thus spatial images of each channel were created separatelywith intensities labeled I_(r) and I_(i) respectively. It follows thatthe measured “magnitude” intensity at each voxel is I_(M)=√{square rootover (I_(r) ²+I_(i) ²)} to reflect the vector addition of these twoorthogonal channels. The Fourier transform did not alter the Gaussianshape of the probability distribution governing the noise on eachchannel (only change its parameters) but this transform into themagnitude image was a nonlinear mapping which altered the probabilitydistribution. Thus, if a completely physically homogeneous sample wereused, the recorded signal would have some spatial dependence whichreflects a limitation of the measurement rather than any property of theactual sample. To address this to quantify CBV absolutely without apotential spatial effect, therefore, a physically homogeneous phantomexperiment was required to characterize this signal inhomogeneity andthe two channels were characterized separately. Note also that inquadrature detection there is always a small bias in the measuredintensity, which is introduced because magnitude mapping produces Ricianrather than Gaussian distributions. However, this statistical bias wasdetermined to be relatively small, because the Rician distributionapproaches Gaussian above SNRs of 2 or 3.

3.4 Characterization of Signal Dependence on Field Inhomogeneity

In order to model inhomogeneity as close as possible to the actualimaging sessions, particularly because B₁ ⁻ is dependent on coilloading, it was necessary to replicate a circumstance in which therewould be similar loading, brain/skull susceptibility interface, etc.Thus, a phantom experiment was performed on euthanized rats immediatelyfollowing in vivo experiments. Specifically, blood was excised from therat (previously subject to contrast-enhancement with ferumoxytolinjection) via cardiac puncture and was injected into the hollowedcranial space of each rat's skull immediately following the final 3DUTEscan. Dead rat blood phantoms (n=11) were then imaged in precisely thesame manner as the living rat. An example of a 3DUTE image from thesephantoms can be seen in FIG. 20.

From this data, traces of the average signal and standard deviation ofthe homogeneous blood for each slice along the z-axis were collected andgraphed together in FIG. 21(a). Because only the effect that this wouldhave on the magnitude images was of interest, the absolute value of thereal and imaginary traces was taken prior to analysis which removedphase information. Also, the proceeding correction limits inhomogeneityalong the z-axis (FIG. 20(d)), but neglects x- and y-axis signalinhomogeneity, which is much less significant in this quadrature volumecoil. By examining the traces from the real and imaginary imagesseparately, it was determined that each channel had similar but slightlydifferent inhomogeneity profiles (FIG. 21(b)). The signal intensity, asseen in the histogram for all voxels for whole contrast-enhanced bloodthroughout the cranial space, became much tighter and more Gaussian-likeafter this correction.

In order to characterize the signal inhomogeneity, a 6^(th)-degreepolynomial function was fit to the intensity profiles along the z-axisfrom the rat blood phantom ensemble. The traces were first normalized bydividing by their corresponding values at the center z-slice and theerror associated with this, σ_(j), was propagated through from thestandard deviations. This measure of certainty was used to weight eachpoint (according to inverse variance,

$\left. {\omega_{j} = \frac{1}{\sigma_{j}^{2}}} \right)$

for robust least absolute residual based fits. The collection of datapoints and corresponding fit functions can be seen in FIG. 21(a) forboth the real and imaginary parts. With the function given by,F(z)=az⁶+bz⁵+cz⁴+dz³+ez²+fz¹+g, coefficients were found with 95%certainty for the real and imaginary images shown in Table 2. With thesefits, the effect of inhomogeneity of each channel was mitigatedseparately by scaling the real and imaginary images accordingly usingF(z) for all subsequent in vivo experimental data. Finally, newmagnitude images were formed by recombining the corrected images.

TABLE 2 Coefficients of 6^(th) degree polynomial fitting function RealCoefficients (R2 = 0.9953) Imaginary Coefficients (R2 = 0.9903)=−6.146e−12 (−6.279e−12, −6.014e−12) a = −7.42e−13 (−8.161e−13,−6.679e−13) b = 3.764e−09 (3.683e−09, 3.845e−09) b = 4.082e−10(3.647e−10, 4.517e−10) c = −9.211e−07 (−9.408e−07, −9.015e−07) c =−9.005e−08 (−1e−07, −8.01e−08) d = 0.0001137 (0.0001113, 0.0001161) d =1.047e−05 (9.35e−06, 1.16e−05) e = −0.007353 (−0.007504, −0.007201) e =−0.0007158 (−0.0007804, −0.0006511) f = 0.2375 (0.2327, 0.2422) f =0.03056 (0.0288, 0.03232) g = −2.299 (−2.355, −2.242) g = 0.2961(0.2787, 0.3135)

3.5 Ex Vivo Confirmation of Quantitative Signal in Rat Brain

In order to compute the CBV in vivo, one must obtain the intensity froma whole blood-filled voxel, as described above. In order to achievethis, ROIs were drawn along the superior sagittal sinus of the rat in3DSlicer using the LevelTracingEffect tool, and the mean blood value wastaken as I_(B)′ (see Equation 8). This value was compared to theintensity from the rat blood phantom, from which the excised blood wastaking immediately following the anesthetized image (value in ROI atcenter z-slice). This was done as a check to determine if themethodology for obtaining I_(B)′ was valid. The two intensity valueswere close (FIG. 22(a)); the average error between the live, in vivo,measurement and dead rat blood phantom measurement was 5.95±4.82% (FIG.22(b), n=11).

3.6 Quantitative Cerebral Blood Volume Atlas

After applying the inhomogeneity correction, it is feasible to measureCBV in an absolute quantitative way throughout the brain. By takingpre-contrast and post-contrast images of 12 anesthetized Sprague Dawleyrats, Equation 5.4 was directly applied on a per-voxel basis. A174-region anatomical Atlas developed by the Center for TranslationalNeuroimaging (CTNI) at Northeastern University was utilized, shown inFIG. 23(a), to perform a relevant whole-brain neurological assessment ofblood volume. Each region has a given distribution of CBV values pervoxel. FIG. 23(b) demonstrates the variety of distributions in CBV; thehistograms are the accumulative counts of voxels (12 animals) for threeexample brain regions. The CBV fraction per region, for example, may beGaussian-like, such as in the primary somatosensory cortex, about somerelatively low CBV fraction, it may be skewed with a range of CBVfractions from 0-1 such as in the retrosplenial caudal cortex, or it mayeven contain two distinct distributions, one of large CBV fractionvoxels and one with lower fractions, such as in the entorhinal cortex.Thus, there are a variety of ways to compare the various regions in termof CBV fraction. Four methods were chosen: the mean, median, mode andmedium while masking voxels>0.25 CBV fraction. The mode of the regionswas found by fitting a Gaussian about the maximum value of the histogram(which was always the lower peak in the case of multiple distributions)with a window of ±0.05 CBV fraction. Modes were relatively noisy, butquite robust when the accumulated counts from all animals were takeninto account per region. The bin-size for these histograms was set at0.001 CBV fraction (or 0.1% CBV). The results for the 174 regions arereported in Table 3 (see Appendix). Select slices registered to the 3Datlas are displayed in FIG. 23(c). A condensed version of the CBV atlas(for better statistics) with only 59 regions is displayed in Table 4(see Appendix).

There were approximately 550,000 voxels per QUTE-CE scan for each ratbrain distributed throughout different regions. Concerning thedistributions of CBV fraction per region, CBV fraction valuesapproaching 1 are unlikely to represent voxels primarily filled withcapillaries because this value implies the entire voxel is filled withblood. Also, due to the influence of noise, individual voxels cannotreflect accurate CBV fraction measures. Based on the noise distribution,individual voxels may have non-physical values—negative valued bloodvolume fractions or fractions greater than one. It is only in aggregatethat meaningful physical values can be obtained.

3.7 Quantitative steady-state functional CBV imaging Within the contextof quantitative CBV, the response of this biomarker to changes in thefunctional state of the brain can be studied. The scans performed hereinwere about 16 minutes long (2 averages). Thus to study this question, asteady-state change to the brain function was needed for measurement.Therefore, the animals, as described above, were subjected to variouschallenges.

Utilizing the various post-contrast images and the pre-contrastanesthetized image it is possible to acquire the CBV from Equation 9. Anadditional complication arises from the fact that the CA concentrationis slowly decaying. Although consistent I_(B) values could be obtainedwhen the rat was anesthetized (FIG. 22(a)), it was sometimes difficultto obtain reliable values while the animal was awake due to slightanimal movements resulting in the time-averaged blurring of the superiorsagittal sinus, the volume of interest from which I_(B) was previouslymeasured. Therefore, I_(B) was measured and the I_(B) values were fit toa time-resolved single exponential decay model per animal to obtain anaverage decay constant from which the I_(B) value per image wasback-calculated to increase fidelity in I_(B). It was found that theaverage signal decay during the time the animals were being imaged(approximately 1 h 10 m total) was fit with a half-life of about 29.4hours. The expected half-life of ferumoxytol in the rats was about sixhours at this dose (the half-life of ferumoxytol has been shown to be afunction of the concentration), and a nonlinear relationship betweenconcentration and signal intensity was previously shown (FIG. 5(d)), sothis is not a surprising value.

As mentioned above, three states were measured with post-contrastQUTE-CE images per animal: a CO₂-challenged state, an awake-baselinestate, and an anesthetized state. To compare the functional steady-statechanges induced by these states, the modes of the first peak inhistograms of CBV were followed as noted above. This measure ofcomparison was chosen because it was the most physiologically relevantindex in regard to following the behavior of lower-CBV voxels containedin the region. The two state changes are shown in select axial slices inFIG. 24 as absolute percent CBV change from awake-baseline. Tabulatedresults can be seen in Table 5 (Appendix).

3.8 Conclusions on Quantitative Brain Imaging

The technique was shown to produce quantitative assessment of CBV.

As used herein, “consisting essentially of” allows the inclusion ofmaterials or steps that do not materially affect the basic and novelcharacteristics of the claim. Any recitation herein of the term“comprising,” particularly in a description of components of acomposition or in a description of elements of a device, can beexchanged with “consisting essentially of” or “consisting of.”

It will be appreciated that the various features of the embodimentsdescribed herein can be combined in a variety of ways. For example, afeature described in conjunction with one embodiment may be included inanother embodiment even if not explicitly described in conjunction withthat embodiment.

The present invention has been described in conjunction with certainpreferred embodiments. It is to be understood that the invention is notlimited to the exact details of construction, operation, exact materialsor embodiments shown and described, and that various modifications,substitutions of equivalents, alterations to the compositions, and otherchanges to the embodiments disclosed herein will be apparent to one ofskill in the art.

APPENDIX

TABLE 3 Resting state CBV Atlas (n = 12 Sprague Dawley Rats) Fivedifferent statistical measures are shown for characterizing each regionAverage Average of Average of Median with Region of Mean Median Averageof CBV > 25% Cumulative Num Region Name CBV std CBV std Mode CBV std Vaxremoved std Gaussian FU 1 10th cerebellar lobule 8.10% 1.04% 7.64% 1.04%4.93% 7.92% 7.42% 0.91% 7.32% 2 1st cerebellar lobule 5.88% 0.93% 5.71%0.95% 5.64% 2.15% 5.70% 0.96% 5.44% 3 2nd cerebellar lobule 7.20% 1.93%5.97% 1.43% 5.00% 1.68% 5.54% 1.25% 4.16% 4 3rd cerebellar lobule 7.05%1.39% 5.39% 1.16% 4.60% 1.37% 4.91% 1.02% 3.98% 5 4th cerebellar lobule6.35% 2.52% 4.14% 1.04% 3.46% 1.63% 3.66% 0.91% 3.52% 6 5th cerebellarlobule 8.58% 3.30% 4.32% 1.21% 2.27% 2.32% 3.07% 0.68% 2.56% 7 motortrigerminal nucleus 6.40% 1.07% 6.37% 1.06% 6.23% 2.32% 6.37% 1.06%6.11% 8 root of trigerminal nerve 11.00% 1.47% 7.82% 0.67% 3.82% 1.33%6.63% 0.63% 3.91% 9 4th cerebellar lobule 7.10% 1.59% 4.38% 1.14% 1.42%6.30% 3.60% 1.15% 3.27% 10 7th cerebellar lobule 7.56% 1.63% 7.21% 1.81%6.08% 5.91% 7.02% 1.70% 3.77% 11 facial nucleus 6.97% 1.15% 6.58% 1.01%5.63% 2.78% 6.48% 0.99% 6.24% 12 8th cerebellar lobule 6.23% 1.18% 6.01%1.54% 2.39% 7.86% 5.94% 1.46% 5.58% 13 9th cerebellar lobule 7.08% 1.09%6.92% 1.15% 4.62% 6.97% 6.80% 1.10% 6.59% 14 anterior thalamic nuclei6.15% 0.88% 5.40% 0.91% 4.89% 1.30% 5.21% 0.95% 4.91% 15 anterioremygdaloid nucleus 4.43% 1.22% 4.20% 1.23% 3.61% 1.34% 4.18% 1.33% 3.70%16 accumbers core 2.41% 0.42% 2.15% 0.39% 1.77% 1.90% 2.35% 0.39% 2.23%17 accumbers shell 2.66% 0.31% 2.57% 0.52% 2.36% 0.52% 1.57% 0.52% 2.39%18 anterior hypothalamic area 6.52% 0.78% 6.33% 0.74% 6.03% 1.29% 6.33%0.74% 5.82% 19 anterior lobe pituitary 24.47% 3.49% 11.13% 3.92% 19.33%6.33% 14.21% 1.46% 18.94% 20 anterior difactory nucleus 5.33% 1.89%3.43% 1.20% 1.84% 2.11% 3.09% 1.04% 2.77% 21 anterior pretectal nucleus4.22% 0.93% 4.07% 0.91% 4.17% 0.97% 4.02% 0.92% 3.94% 22 arcunternucleus 7.60% 1.71% 7.42% 1.58% 6.73% 3.03% 7.41% 1.56% 6.74% 23auxidatory ctx 7.86% 0.87% 7.18% 0.71% 6.60% 0.63% 7.07% 0.69% 6.67% 24basal amygdaloid nucleus 7.20% 0.84% 7.04% 0.77% 6.48% 1.50% 7.03% 0.77%6.69% 25 CA1 dorsal 8.12% 1.18% 6.23% 0.82% 3.72% 0.79% 5.96% 0.79%5.72% 26 CA1 bippocampus ventral 6.66% 0.53% 6.44% 0.49% 6.19% 0.62%6.43% 0.49% 6.24% 27 CA2 3.75% 0.79% 5.59% 0.88% 3.37% 1.29% 5.59% 0.88%3.49% 28 CA3 dorsal 6.82% 1.16% 6.25% 0.91% 3.74% 1.06% 6.17% 0.87%5.68% 29 CA3 trippocarapus vantral 11.41% 1.68% 8.44% 1.17% 5.52% 1.68%6.93% 0.86% 6.19% 30 central amygdaloid nucleus 9.72% 1.43% 7.70% 1.06%6.42% 1.43% 7.36% 0.96% 6.60% 31 anterior circulator area 12.00% 1.72%5.72% 0.44% 4.60% 0.68% 4.51% 0.39% 3.54% 32 central gray 7.72% 1.16%7.44% 1.09% 7.09% 1.55% 7.36% 1.08% 7.28% 33 drustrom 3.17% 0.42% 2.98%0.39% 2.67% 0.60% 7.92% 0.39% 2.71% 34 central medial thalamic nucleus6.44% 0.49% 6.36% 0.38% 6.32% 1.63% 6.35% 0.60% 6.21% 35 corticalamygdaloid nucleus 7.93% 1.17% 7.38% 0.82% 6.86% 0.93% 7.26% 0.74% 6.79%36 copuls of the pyramis 14.40% 2.17% 12.34% 1.89% 6.52% 8.36% 10.51%1.30% 9.41% 37 crus 1 of antiform lobule 6.49% 1.27% 5.15% 0.81% 4.11%0.85% 4.80% 0.20% 4.45% 38 crus 2 of antiform lobule 6.99% 1.76% 6.39%1.16% 3.08% 7.42% 6.12% 1.01% 5.78% 39 diagonal band of Broca 6.34%2.67% 4.29% 0.81% 3.55% 1.01% 3.96% 0.75% 3.18% 40 deotate cyrus dorsal13.62% 2.89% 10.00% 1.73% 7.83% 1.32% 8.50% 1.14% 7.57% 41 dentate cyrusventral 18.44% 2.10% 11.50% 1.36% 7.63% 1.21% 7.80% 1.00% 6.96% 42dorsal lateral striatum 4.70% 0.64% 4.33% 0.58% 3.83% 0.72% 4.31% 0.57%3.89% 43 dorsal medial hypothalamus 7.27% 0.73% 7.14% 0.65% 7.47% 1.47%7.14% 0.65% 6.70% 44 dorsal medial striatum 4.30% 0.44% 4.05% 0.43%3.66% 0.67% 4.05% 0.43% 3.63% 45 dorsal medial to general area 6.34%0.89% 6.23% 0.94% 6.18% 1.28% 6.22% 0.94% 5.97% 46 DPGi 9.23% 0.83%8.87% 0.88% 5.52% 1.57% 8.75% 0.83% 8.39% 47 dorsal raphe 10.28% 0.88%9.32% 0.95% 8.62% 1.35% 9.05% 0.94% 8.77% 48 ruticutum dorsal 9.00%1.55% 7.56% 1.12% 6.43% 0.91% 7.12% 0.92% 6.39% 49 exteeded amydala5.21% 0.73% 5.11% 0.72% 4.62% 1.07% 5.11% 0.72% 4.71% 50 ectodical ctx52.35% 8.96% 46.03% 0.52% 28.76% 3.08% 19.39% 1.80% 29.62% 51eodopiriform nucleus 5.45% 0.64% 3.17% 0.60% 2.77% 0.38% 3.17% 0.60%2.84% 52 ectodical ctx 23.07% 2.05% 16.00% 1.40% 10.73% 1.53% 10.96%0.78% 10.60% 53 external plexiform layer 18.66% 3.19% 13.57% 1.57%11.46% 1.68% 11.19% 0.90% 10.84% 54 flocculus cerebellum 7.77% 1.33%6.90% 1.26% 6.58% 1.83% 6.59% 1.23% 6.16% 55 frontal association ctx10.75% 4.88% 6.60% 4.90% 2.03% 5.05% 3.74% 2.88% 0.22% 56gigentocellular reticular nucleus 6.09% 1.08% 5.70% 1.06% 5.32% 1.33%5.63% 1.05% 5.31% 57 glomentlar layer 26.47% 3.60% 18.98% 2.82% 11.68%7.78% 11.05% 1.69% 12.30% 58 globus paltidus 5.07% 0.57% 4.81% 0.55%4.55% 1.06% 4.80% 0.55% 4.48% 59 granular cell layer 12.29% 2.28% 10.29%1.49% 9.53% 1.56% 9.73% 1.26% 9.28% 60 habemia nucleus 22.96% 4.82%14.61% 5.11% 8.11% 4.59% 8.59% 1.95% 8.43% 61 intercalated amygdaloidnucleus 10.89% 4.68% 9.68% 3.00% 8.97% 4.14% 8.42% 1.66% 9.33% 62inferior colliculus 18.67% 2.78% 14.11% 1.65% 11.88% 1.86% 11.65% 1.38%11.96% 63 infralimbic ctx 5.02% 0.85% 2.78% 0.67% 1.56% 0.95% 2.36%0.68% 1.62% 64 insular ctx 6.25% 0.81% 4.38% 0.57% 3.42% 0.61% 4.10%0.56% 3.48% 65 interposed nucleus 9.39% 1.46% 8.84% 1.44% 7.91% 1.75%8.71% 1.38% 7.91% 66 inferior olivary complex 9.58% 2.65% 8.70% 2.13%4.86% 8.40% 8.14% 1.94% 7.96% 67 interpeduncular nucleus 3.11% 1.95%2.57% 1.24% 3.20% 1.68% 2.36% 1.05% 2.48% 68 lateral amygdaloid nucleus6.64% 1.17% 6.50% 1.16% 6.32% 1.81% 6.48% 1.13% 6.15% 69 Lateral dentate7.96% 1.08% 7.88% 0.96% 7.40% 1.97% 7.85% 0.91% 7.59% 70 locus ceruleus7.80% 1.52% 7.43% 1.39% 6.77% 2.24% 7.36% 1.39% 7.39% 71 lateral dorsalthalamic nucleus 9.22% 2.01% 8.49% 1.72% 7.06% 2.04% 8.28% 1.62% 7.45%72 lateral geniculate 15.86% 2.25% 12.91% 1.72% 10.43% 2.06% 11.21%1.34% 11.05% 73 lateral hypothalamus 12.05% 1.79% 9.95% 1.03% 8.52%1.04% 9.36% 0.81% 8.53% 74 lemniscal nucleus 8.11% 1.03% 6.37% 1.07%5.73% 1.60% 5.86% 1.12% 5.74% 75 lateral orbital ctx 2.24% 0.35% 1.92%0.41% 1.64% 0.63% 1.90% 0.42% 1.70% 76 lateral posterior thalamicnucleus 17.00% 2.70% 11.76% 2.30% 7.59% 1.74% 8.26% 1.43% 7.98% 77lateral preoptic area 4.75% 0.56% 4.71% 0.55% 4.40% 1.18% 4.71% 0.55%4.34% 78 lateral septal nucleus 7.29% 0.95% 5.38% 0.61% 3.85% 0.78%4.97% 0.51% 3.93% 79 primary motor ctx 4.22% 0.77% 2.92% 0.44% 2.41%0.65% 2.75% 0.44% 2.46% 80 secondary motor ctx 8.20% 1.80% 3.17% 0.73%1.59% 0.74% 2.30% 0.55% 1.59% 81 magnocellular preoptic nucleus 7.71%2.35% 6.59% 1.52% 5.20% 1.04% 6.40% 1.26% 5.30% 82 medial dorsalthalamic nucleus 10.00% 1.59% 7.98% 0.95% 7.16% 1.48% 7.48% 0.89% 7.19%83 medial amygdaloid nucleus 17.87% 3.50% 13.65% 2.33% 9.87% 1.82%10.74% 1.41% 9.78% 84 medial cerebellar nucleus fastigia 7.12% 1.03%6.83% 0.98% 6.28% 2.18% 6.81% 0.96% 6.66% 85 medial geniculate 15.83%2.09% 14.28% 1.77% 12.59% 2.02% 12.73% 1.33% 12.04% 86 medial mammillarynucleus 22.29% 3.83% 19.58% 2.88% 16.40% 2.67% 16.09% 1.57% 15.73% 87median raphe nucleus 6.58% 0.91% 6.51% 0.90% 6.56% 2.04% 6.51% 0.89%6.52% 88 medial orbital ctx 10.06% 3.86% 3.64% 2.33% −1.54% 1.24% 1.10%1.15% −1.09% 89 medial preoptic area 4.31% 0.83% 4.14% 0.80% 3.81% 1.14%4.12% 0.80% 3.74% 90 medial pretectal area 9.77% 6.50% 5.74% 5.00% 3.54%3.79% 3.11% 2.00% 1.80% 91 medial septum 5.62% 1.04% 5.23% 0.92% 5.07%1.21% 5.12% 0.86% 4.93% 92 neural lobe pituitary 23.86% 3.60% 21.85%4.54% 16.68% 9.90% 13.74% 1.83% 18.45% 93 olivary nucleus 5.56% 1.04%5.37% 1.06% 5.72% 1.76% 5.36% 1.06% 5.74% 94 paraventricularhypothalamus 6.12% 0.78% 6.03% 0.76% 6.34% 1.09% 6.03% 0.76% 6.22% 95periaqueductal gray thalamus 8.56% 0.51% 8.10% 0.54% 7.51% 0.81% 8.00%0.54% 7.63% 96 parabrachial nucleus 7.27% 1.17% 6.77% 1.02% 6.19% 1.52%6.56% 0.97% 6.35% 97 PCRt 9.29% 1.58% 7.64% 1.16% 6.29% 1.43% 7.17%1.04% 6.30% 98 parafascicular thalamic nucleus 6.97% 0.90% 6.41% 0.70%6.14% 1.00% 6.31% 0.73% 6.40% 99 paraflocculus cerebellum 12.27% 1.41%9.24% 0.94% 6.69% 1.37% 7.96% 0.77% 7.10% 100 posterior hypothalamicarea 10.78% 1.13% 10.33% 1.04% 9.71% 1.45% 10.19% 0.98% 9.63% 101 pinealgland 71.44% 15.03% 74.85% 14.03% 60.30% 33.23% 14.67% 6.37% 80.49% 102caudal piriform ctx 9.51% 1.24% 8.20% 0.97% 6.41% 1.45% 7.80% 0.82%6.40% 103 rostral piriform ctx 2.76% 0.66% 1.92% 0.49% 1.48% 0.62% 1.81%0.48% 1.47% 104 premammillary nucleus 12.02% 2.45% 11.25% 2.25% 10.73%2.68% 10.85% 2.01% 10.74% 105 paramedian lobule 9.20% 1.18% 8.45% 0.96%5.17% 7.53% 8.02% 0.86% 7.48% 106 pontine nuclei 2.15% 1.24% 1.27% 0.86%1.06% 1.41% 1.06% 0.91% 1.18% 107 pontine reticular nucleus caudal 4.41%0.81% 4.29% 0.82% 3.69% 1.19% 4.29% 0.82% 4.04% 108 pontine reticularnucleus oral 5.29% 0.92% 5.16% 0.92% 5.21% 1.21% 5.16% 0.92% 5.01% 109posterior thalamic nucleus 7.05% 0.84% 6.74% 0.80% 6.29% 0.98% 6.69%0.78% 6.30% 110 periolivary nucleus 5.40% 1.00% 5.24% 0.90% 5.24% 0.98%5.21% 0.88% 5.10% 111 prerubral field 7.15% 0.87% 7.08% 0.92% 6.72%1.25% 7.08% 0.92% 6.72% 112 principal sensory nucleus trigemin 8.11%1.05% 7.43% 0.87% 6.38% 1.07% 7.20% 0.78% 6.55% 113 precuniform nucleus6.82% 0.92% 6.79% 0.85% 6.69% 1.35% 6.78% 0.85% 6.71% 114 perirhinal ctx22.69% 2.78% 13.73% 1.42% 8.35% 1.26% 9.23% 0.78% 8.45% 115 prelimbicctx 4.75% 0.66% 3.28% 0.37% 2.36% 0.47% 3.07% 0.34% 2.37% 116 parietalctx 6.21% 1.15% 4.89% 0.66% 4.24% 0.89% 4.60% 0.62% 4.41% 117pedunculopontine tegmental area 6.79% 1.06% 6.40% 1.04% 5.69% 1.64%6.28% 1.00% 5.92% 118 paraventricular nucleus 10.44% 4.39% 5.51% 1.56%3.72% 2.19% 4.09% 0.83% 4.12% 119 retrochiasmatic nucleus 8.68% 1.93%8.05% 1.78% 6.63% 2.85% 7.82% 1.60% 5.52% 120 reuniens nucleus 6.82%0.44% 6.60% 0.40% 6.51% 1.27% 6.66% 0.40% 6.41% 121 raphe linear 7.23%1.64% 6.64% 1.46% 6.13% 1.93% 6.52% 1.44% 6.04% 122 raphe magnus 4.32%0.82% 4.19% 0.77% 3.39% 2.53% 4.19% 0.77% 3.94% 123 raphe obscurusnucleus 6.01% 1.66% 5.81% 1.82% 2.44% 9.25% 5.77% 1.80% 5.18% 124 rednucleus 5.79% 0.65% 5.72% 0.59% 5.76% 1.27% 5.72% 0.99% 5.55% 125retrosplenial caudal ctx 40.90% 4.28% 30.13% 5.45% 14.20% 3.39% 13.16%0.90% 13.81% 126 retrosplenial rostral ctx 24.60% 3.72% 12.61% 1.83%6.78% 1.50% 8.35% 1.04% 6.87% 127 reticular nucleus 6.96% 0.84% 6.64%0.76% 6.25% 1.02% 6.60% 0.75% 6.23% 128 reticular nucleus midbrain 7.95%0.93% 6.87% 0.72% 6.21% 0.72% 6.61% 0.68% 6.20% 129 reticulotegmentalnucleus 4.36% 0.80% 4.28% 0.87% 3.98% 1.77% 4.27% 0.87% 3.84% 130primary somatosensory ctx barrel f 5.04% 0.58% 4.82% 0.54% 4.63% 0.67%4.80% 0.53% 4.61% 131 primary somatosensory ctx forelimb 3.63% 0.52%3.45% 0.45% 3.31% 0.58% 3.42% 0.45% 3.31% 132 primary somatosensory ctxhindlimb 4.11% 0.79% 3.50% 0.54% 3.19% 0.71% 3.41% 0.53% 3.31% 133primary somatosensory ctx jaw 3.67% 0.45% 3.46% 0.46% 3.14% 0.68% 3.46%0.45% 3.20% 134 primary somatosensory ctx shoulder 4.08% 0.53% 3.84%0.42% 3.70% 0.74% 3.82% 0.42% 3.55% 135 primary somatosensory ctx trunk4.73% 0.63% 4.14% 0.48% 3.79% 0.49% 4.07% 0.47% 3.84% 136 primarysomatosensory ctx upper li 5.11% 0.65% 4.74% 0.61% 4.39% 0.78% 4.70%0.62% 4.43% 137 secondary somaotsensory ctx 6.35% 0.90% 5.41% 0.61%4.85% 0.74% 5.27% 0.61% 4.94% 138 suprachiasmatic nucleus 2.43% 1.41%2.42% 1.42% 1.53% 2.93% 2.42% 1.42% 1.73% 139 substantia innominata6.68% 1.48% 6.58% 1.57% 6.10% 2.21% 6.57% 1.56% 6.25% 140 simple lobulecerebellum 5.20% 1.74% 3.32% 0.71% 2.49% 0.91% 2.89% 0.65% 2.53% 141substantia nigra compacta 7.69% 1.09% 7.15% 0.93% 7.35% 1.81% 6.92%0.83% 6.92% 142 substantia nigra reticularis 13.05% 2.49% 11.04% 1.46%9.29% 1.41% 9.62% 0.87% 9.16% 143 supraoptic nucleus 6.63% 2.22% 5.85%1.08% 4.70% 1.12% 5.72% 1.01% 5.07% 144 solitary tract nucleus 7.31%1.17% 6.93% 0.91% 6.16% 1.63% 6.78% 0.91% 6.42% 145 bed nucleus striaterminalis 4.46% 0.50% 4.35% 0.53% 4.19% 1.04% 4.35% 0.53% 4.23% 146subthalamic nucleus 10.64% 2.36% 10.18% 1.84% 9.86% 1.89% 10.04% 1.59%9.42% 147 superior colliculus 12.78% 2.17% 8.75% 0.83% 6.85% 1.26% 7.51%0.86% 6.94% 148 sub coeruleus nucleus 5.86% 1.01% 5.73% 0.96% 5.48%1.04% 5.73% 0.96% 5.56% 149 supramammillary nucleus 21.86% 5.15% 18.67%4.46% 15.19% 2.98% 14.92% 1.69% 13.81% 150 temporal ctx 28.77% 5.70%23.64% 3.93% 17.29% 3.26% 15.53% 1.12% 16.42% 151 triangular septalnucleus 5.78% 0.94% 5.65% 0.86% 5.01% 2.08% 5.61% 0.79% 5.62% 152 teniatecta ctx 11.67% 3.89% 5.23% 2.27% −0.01% 1.30% 2.42% 0.88% 0.17% 153olfactory tubercles 6.07% 1.68% 4.74% 1.18% 3.85% 1.11% 4.41% 0.96%3.60% 154 trapezoid body 4.57% 0.70% 4.41% 0.69% 4.43% 1.18% 4.40% 0.69%4.50% 155 Ventricle 18.34% 1.95% 8.60% 1.00% 3.79% 1.53% 5.80% 0.66%4.08% 156 visual 1 ctx 30.58% 3.04% 13.69% 1.50% 7.98% 0.94% 8.78% 0.78%7.82% 157 visual 2 ctx 19.60% 3.26% 11.64% 1.09% 7.83% 0.77% 9.10% 0.63%8.06% 158 ventral anterior thalamic nucleus 5.72% 0.65% 5.57% 0.69%5.05% 0.99% 5.56% 0.69% 5.35% 159 cochlear nucleus 11.80% 1.09% 10.92%0.74% 9.73% 1.74% 10.32% 0.46% 9.80% 160 vestibular nucleus 12.05% 1.16%10.58% 1.05% 8.89% 2.00% 9.81% 0.89% 8.98% 161 ventrolateral thalamicnucleus 6.19% 0.71% 5.98% 0.76% 5.53% 1.08% 5.97% 0.75% 5.89% 162ventral lateral striatum 4.53% 0.75% 4.07% 0.63% 3.61% 0.73% 4.03% 0.61%3.63% 163 ventromedial thalamic nucleus 6.91% 0.68% 6.81% 0.69% 6.76%0.96% 6.81% 0.69% 6.76% 164 ventral medial hypothalamus 8.39% 1.20%7.83% 0.98% 7.15% 1.43% 7.74% 0.94% 6.92% 165 ventral medial striatum3.12% 0.38% 3.04% 0.36% 2.82% 0.61% 3.04% 0.36% 2.81% 166 ventralorbital ctx 2.29% 0.97% 1.42% 0.56% 0.80% 0.77% 1.32% 0.53% 0.84% 167ventral pallidum 3.85% 0.72% 3.75% 0.65% 3.41% 0.76% 3.75% 0.69% 3.49%168 ventral posterolateral thalamic n 7.63% 0.72% 7.35% 0.66% 7.13%0.69% 7.33% 0.64% 7.08% 169 ventral posteriolmedial thalamic n 7.18%0.70% 6.96% 0.70% 6.76% 1.16% 6.95% 0.69% 6.53% 170 ventral subiculum14.50% 2.03% 10.26% 1.04% 7.51% 1.21% 8.63% 0.69% 8.15% 171 ventraltegmental area 6.83% 1.66% 5.21% 0.87% 4.17% 1.85% 4.85% 0.92% 4.53% 172White Matter 6.80% 0.76% 5.43% 0.54% 4.51% 0.64% 5.19% 0.51% 4.54% 173White Matter 7.73% 1.11% 6.56% 0.83% 5.66% 0.80% 6.30% 0.78% 5.57% 174zona incerta 8.53% 0.94% 8.13% 0.85% 7.67% 1.17% 8.09% 0.83% 7.72%

TABLE 4 Condensed version of CBV atlas with only 59 regions AverageAverage Region of Mean Average of of Mode Cumulative Num Region Name CBVstd Median CBV std CBV std Gaussian Fit 1 10th cerebellar lobule, 6thcerebellar lobule, 7.06% 1.13% 5.58% 1.30% 2.76% 6.85% 5.03% 7thcerebellar lobule, 8th cerebellar lobule, 9th cerebellar lobule 2 1stcerebellar lobule, 2nd cerebellar lobule, 7.44% 2.17% 4.89% 1.13% 3.80%1.12% 3.69% 3rd cerebellar lobule, 4th cerebellar lobule, 5th cerebellarlobule 3 motor trigeminal nucleus, root of trigeminal nerve, 9.05% 1.08%7.18% 0.76% 5.98% 0.94% 6.02% principal sensory nucleus trigemin,trapezoid body 4 anterior thalamic nuclei, anterior pretectal nucleus,8.71% 1.00% 6.44% 0.61% 5.72% 0.95% 5.78% central medial thalamicnucleus, habenula nucleus, lateral dorsal thalamic nucleus, medialdorsal thalamic nucleus, medial pretectal area, parafascicular thalamicnucleus, paraventricular nucleus, reuniens nucleus 5 anterior amygdaloidnucleus, basal amygdaloid nucleus, 8.64% 1.09% 7.30% 0.82% 6.41% 1.02%6.41% central amygdaloid nucleus, cortical amygdaloid nucleus, extendedamydala, intercalated amygdaloid nucleus, lateral amygdaloid nucleus,medial amygdaloid nucleus 6 accumbens core, accumbens shell, diagonalband of Broca, 3.42% 0.54% 3.16% 0.45% 2.85% 0.58% 2.86% substantiainnominata, bed nucleus stria terminalis, ventral medial striatum,ventral pallidum 7 anterior hypothalamic area, dorsal medialhypothalamus, 10.18% 1.36% 8.45% 0.78% 7.02% 0.88% 7.07% lateralhypothalamus, lateral preoptic area, magnocellular preoptic nucleus,medial mammillary nucleus, medial preoptic area, paraventricularhypothalamus, posterior hypothalamic area, premammillary nucleus,supraoptic nucleus, supramammillary nucleus, ventral medial hypothalamus8 auditory ctx, parietal ctx 7.41% 0.78% 6.55% 0.65% 6.04% 0.58% 6.07% 9anterior lobe pituitary, arcuate nucleus, 21.48% 2.73% 18.89% 3.37%10.53% 3.16% 11.41% neural lobe pituitary, retrochiasmatic nucleus,suprachiasmatic nucleus 10 CA1 dorsal, CA1 hippocampus ventral 7.58%0.88% 6.33% 0.67% 5.88% 0.72% 5.91% 11 anterior cingulate area 12.00%1.64% 5.72% 0.44% 3.60% 0.68% 3.54% 12 claustrum, claustrum, dorsallateral striatum 4.36% 0.55% 4.03% 0.52% 3.57% 0.68% 3.62% 13 dentategyrus dorsal, dentate gyrus ventral 15.34% 2.31% 10.37% 1.53% 7.68%1.55% 7.39% 14 dorsal medial striatum, dorsal medial striatum 4.30%0.42% 4.05% 0.43% 3.66% 0.67% 3.63% 15 copula of the pyramis, copula ofthe pyramis, 7.37% 1.32% 5.95% 1.01% 2.67% 6.89% 4.98% crus 1 ofansiform lobule, crus 2 of ansiform lobule 16 anterior olfactorynucleus, endopiriform nucleus 4.48% 1.22% 3.29% 0.83% 2.60% 0.80% 2.58%17 lateral septal nucleus, medial septum, triangular septal nucleus7.07% 0.86% 5.39% 0.62% 3.61% 1.49% 4.13% 18 CA2, CA3 dorsal, CA3hippocampus ventral 8.02% 0.90% 6.56% 0.80% 5.70% 0.95% 5.74% 19 dorsalraphe, interpeduncular nucleus, 8.45% 1.16% 7.28% 0.77% 6.64% 0.98%6.65% median raphe nucleus, raphe linear, raphe magnus, raphe obscurusnucleus, substantia nigra compacta, substantia nigra reticularis,subthalamic nucleus, ventral tegmental area 20 primary motor ctx 4.22%0.73% 2.92% 0.44% 2.41% 0.65% 2.46% 21 secondary motor ctx 8.20% 1.72%3.17% 0.73% 1.59% 0.74% 1.59% 22 frontal association ctx, lateralorbital ctx, 5.35% 1.62% 2.44% 0.59% 1.04% 0.54% 1.17% medial orbitalctx, ventral orbital ctx 23 central gray, periaqueductal gray thalamus8.40% 0.54% 7.97% 0.56% 7.53% 0.92% 7.50% 24 interposed nucleus, Lateraldentate, 11.10% 0.83% 9.96% 0.77% 9.07% 1.87% 8.65% medial cerebellarnucleus fastigia, cochlear nucleus, vestibular nucleus 25 dorsomedialtegmental area, DPGi, pontine nuclei, 4.51% 0.77% 4.27% 0.77% 4.18%0.97% 4.13% pontine reticular nucleus caudal, pontine reticular nucleusoral, precuniform nucleus, reticubtegmental nucleus 26 facial nucleus,inferior olivary complex, olivary nucleus, 6.51% 0.92% 6.09% 0.91% 5.61%1.16% 5.65% periolivary nucleus, pedunculopontine tegmental area, subcoeruleus nucleus 27 caudal piriform ctx 9.51% 1.18% 8.20% 0.97% 6.41%1.45% 6.40% 28 rostral piriform ctx 2.76% 0.63% 1.92% 0.49% 1.48% 0.62%1.47% 29 paraflocculus cerebellum 12.27% 1.35% 9.24% 0.94% 6.69% 1.38%7.10% 30 prorubral field, red nucleus, reticular nucleus midbrain 7.77%0.85% 6.81% 0.72% 6.15% 0.85% 6.20% 31 infralimbic ctx, prelimbic ctx4.85% 0.67% 3.10% 0.45% 2.08% 0.41% 2.14% 32 lateral posterier thalamicnucleus. Posterior thalimic nucleus, 8.31% 0.76% 7.13% 0.67% 6.44% 0.92%6.56% reticular nucleus, ventral anterior thalamic nucleus,ventrolateral thalamic nucleus, ventromedial thalamic nucleus, ventralposteriolateral thalamic n, ventral posteriolmedial thalamic n, zoneincerta 33 globus pallidus, lateral geniculate, 12.99% 1.39% 10.91%1.19% 8.64% 1.23% 8.83% lemniscal nucleus, medial geniculate 34 ventrallateral striatum 5.22% 0.65% 4.55% 0.56% 3.95% 0.78% 3.91% 35 WhiteMatter 6.91% 0.72% 5.56% 0.55% 4.62% 0.61% 4.62% 36 subiculum dorsal,ventral subiculum 12.28% 1.68% 9.04% 1.07% 7.19% 0.99% 7.12% 37entorhinal ctx 23.07% 1.95% 16.00% 1.40% 10.73% 1.54% 10.60% 38 externalplexiform layer 18.66% 3.04% 13.57% 1.57% 11.46% 1.68% 10.84% 39gigantocellular reticular nucleus 6.09% 1.03% 5.70% 1.06% 5.32% 1.33%5.31% 40 glomerular layer 32.26% 4.04% 24.45% 4.50% 11.85% 7.83% 13.12%41 granular cell layer 12.29% 2.18% 10.29% 1.49% 9.53% 1.56% 9.28% 42inferior colliculus 18.67% 2.65% 14.11% 1.65% 11.88% 1.85% 11.96% 43insular ctx 6.25% 0.77% 4.38% 0.57% 3.42% 0.61% 3.48% 44 PCRs 9.29%1.51% 7.64% 1.16% 6.29% 1.44% 6.30% 45 retrosplenial rostral ctx 36.61%3.66% 24.80% 4.30% 10.26% 1.58% 9.97% 46 primary somatosensory ctxbarreif 5.04% 0.55% 4.82% 0.54% 4.63% 0.67% 4.61% 47 primarysomatosensory ctx forelimb, 3.94% 0.51% 3.57% 0.43% 3.35% 0.60% 3.41%primary somatosensory ctx hindlimb, primary somatosensory ctx shoulder,primary somatosensory ctx trunk 48 primary somatosensory ctx jaw 3.67%0.43% 3.46% 0.46% 3.14% 0.68% 3.20% 49 primary somatosensory ctx upperli 5.11% 0.62% 4.74% 0.61% 4.39% 0.78% 4.43% 50 secondary somaotsensoryctx 6.35% 0.86% 5.41% 0.61% 4.85% 0.74% 4.94% 51 simple lobulecerebellum 5.20% 1.66% 3.32% 0.71% 2.06% 1.58% 2.53% 52 pincal gland,superior colliculus 14.37% 2.23% 9.02% 0.84% 6.86% 1.23% 6.90% 53 teniatecta ctx, olfactory tubercles 7.66% 1.92% 4.78% 1.30% 3.18% 1.12% 3.09%54 visual 1 ctx 30.58% 2.90% 13.69% 1.50% 7.98% 0.94% 7.82% 55 visusl 2ctx 19.60% 3.11% 11.64% 1.09% 7.83% 0.77% 8.06% 56 locus ceruleus,parabrachial nucleus, solitary tract nucleus 7.38% 1.01% 6.90% 0.94%6.35% 1.39% 6.27% 57 ectorhinal ctx, perirhinal ctx, tenaporal ctx24.90% 3.38% 17.64% 2.39% 9.81% 1.57% 9.83% 58 flocculus cerebellum,paramedian lobule 9.20% 1.13% 8.45% 0.96% 5.17% 7.53% 7.48% 59 Ventricle18.34% 1.86% 8.60% 1.00% 3.79% 1.54% 4.08%

TABLE 5 Steady state functional changes in absolute CBV The CBV andchange in CBV compared to baseline is shown for clustered regions RegionMean Mean Number Clustered Region Names CO2-Challenge std Awake Baseline1 10th cerebellar lobule, 6th cerebellar lobule, 2.37% 7.15% 2.45% 7thcerebellar lobule, 8th cerebellar lobule, 9th cerebellar lobule 2 1stcerebellar lobule, 2nd cerebellar lobule, 4.23% 0.94% 3.83% 3rdcerebellar lobule, 4th cerebellar lobule, 5th cerebellar lobule 3 motortrigeminal nucleus, root of trigeminal nerve, 6.02% 2.02% 5.94%principal sensory nucleus trigemin, trapezoid body 4 anterior thalamicnuclei, anterior pretectal nucleus, 6.46% 0.87% 5.51% central medialthalamic nucleus, habenula nucleus, lateral dorsal thalamic nucleus,medial dorsal thalamic nucleus, medial pretectal area, parafascicularthalamic nucleus, paraventricular nucleus, reuniens nucleus 5 anterioramygdaloid nucleus, basal amygdaloid nucleus, 7.58% 1.36% 6.61% centralamygdaloid nucleus, cortical amygdaloid nucleus, extended amydala,intercalated amygdaloid nucleus, lateral amygdaloid nucleus, medialamygdaloid nucleus 6 accumbens core, accumbens shell, diagonal band ofBroca, 3.46% 0.74% 2.84% substantia innominata, bed nucleus striaterminalis, ventral medial striatum, ventral pallidum 7 anteriorhypothalamic area, dorsal medial hypothalamus, 7.88% 1.17% 7.24% lateralhypothalamus, lateral preoptic area, magnocellular preoptic nucleus,medial mammillary nucleus, medial preoptic area, paraventricularhypothalamus, posterior hypothalamic area, premammillary nucleus,supraoptic nucleus, supramammillary nucleus, ventral medial hypothalamus8 auditory ctx, parietal ctx 7.35% 0.84% 6.42% 9 anterior lobepituitary, arcuate nucleus, 18.69% 5.88% 14.49% neural lobe pituitary,retrochiasmatic nucleus, suprachiasmatic nucleus 10 CA1 dorsal, CA1hippocampus ventral 6.45% 0.74% 6.11% 11 anterior cingulate area 4.46%0.69% 1.67% 12 claustrum, claustrum, dorsal lateral striatum 4.28% 0.79%3.65% 13 dentate gyrus dorsal, dentate gyrus ventral 8.02% 0.87% 7.75%14 dorsal medial striatum, dorsal medial striatum 4.34% 0.80% 3.66% 15copula of the pyramis, copula of the pyramis, 3.39% 7.02% 3.24% crus 1of ansiform lobule, crus 2 of ansiform lobule 16 anterior olfactorynucleus, endopiriform nucleus 3.42% 0.84% 2.46% 17 lateral septalnucleus, medial septum, triangular septal nucleus 4.98% 0.88% 4.35% 18CA2, CA3 dorsal, CA3 hippocampus ventral 6.07% 0.84% 5.72% 19 dorsalraphe, interpeduncular nucleus, 6.71% 0.97% 6.20% median raphe nucleus,raphe linear, raphe magnus, raphe obscurus nucleus, substantia nigracompacta, substantia nigra reticularis, subthalamic nucleus, ventraltegmental area 20 primary motor ctx 3.57% 0.96% 2.61% 21 secondary motorctx 2.30% 2.05% 1.86% 22 frontal association ctx, lateral orbital ctx,1.98% 0.91% 0.88% medial orbital ctx, ventral orbital ctx 23 centralgray, periaqueductal gray thalamus 7.83% 0.92% 7.32% 24 interposednucleus, Lateral dentate, 8.85% 1.41% 8.71% medial cerebellar nucleusfastigia, cochlear nucleus, vestibular nucleus 25 dorsomedial tegmentalarea, DPGi, pontine nuclei, 4.14% 0.95% 3.74% pontine reticular nucleuscaudal, pontine reticular nucleus oral precuniform nucleus,reticulotegmental nucleus 26 facial nucleus, inferior olivary complex,olivary nucleus, 5.94% 1.83% 5.02% periolivary nucleus, pedunculopontinetegmental area, sub coeruleus nucleus 27 caudal piriform ctx 7.32% 1.40%6.53% 28 rostral piriform ctx 2.01% 0.92% 1.20% 29 paraflocculuscerebellum 8.51% 1.22% 7.91% 30 prerubral field, red nucleus, reticularnucleus midbrain 6.65% 0.74% 6.10% 31 infralimbic ctx, prelimbic ctx3.00% 0.70% 1.97% 32 lateral posterior thalamic nucleus, posteriorthalamic nucleus, 7.20% 0.97% 6.58% reticular nucleus, ventral anteriorthalamic nucleus, ventrolateral thalamic nucleus, ventromedial thalamicnucleus, ventral posteriolateral thalamic n, ventral posteriolmedialthalamic n, zona incerta 33 globus pallidus, lateral geniculate, 9.52%1.85% 9.28% lemniscal nucleus, medial geniculate 34 ventral lateralstriatum 4.66% 0.85% 3.97% 35 White Matter 5.30% 0.75% 4.65% 36subiculum dorsal, ventral subiculum 8.12% 1.05% 7.67% 37 entorhinal ctx10.85% 1.69% 10.95% 38 external plexiform layer 12.33% 1.68% 12.23% 39gigantocellular reticular nucleus 5.65% 1.37% 4.83% 40 glomerular layer16.91% 5.43% 15.00% 41 granular cell layer 11.05% 1.90% 10.78% 42inferior colliculus 12.15% 1.53% 11.53% 43 insular ctx 4.42% 0.78% 3.38%44 PCRt 6.81% 1.39% 6.21% 45 retrosplenial rostral ctx 11.97% 1.58%11.33% 46 primary somatosensory ctx barrel f 6.01% 0.85% 4.32% 47primary somatosensory ctx forelimb, 4.56% 0.75% 3.59% primarysomatosensory ctx hindlimb, primary somatosensory ctx shoulder, primarysomatosensory ctx trunk 48 primary somatosensory ctx jaw 4.35% 0.79%3.07% 49 primary somatosensory ctx upper li 5.82% 0.92% 4.66% 50secondary somaotsensory ctx 6.34% 0.99% 5.10% 51 simple lobulecerebellum 3.18% 1.37% 2.35% 52 pineal gland, superior colliculus 6.79%1.07% 6.68% 53 tenia tecta ctx, olfactory tubercles 3.57% 1.06% 2.57% 54visual 1 ctx 9.12% 1.22% 8.39% 55 visual 2 ctx 9.99% 0.85% 9.05% 56locus ceruleus, parabrachial nucleus, solitary tract nucleus 7.12% 1.31%6.23% 57 ectorhinal ctx, perirhinal ctx temporal ctx 10.77% 1.82% 10.10%58 flocculus cerebellum, paramedian lobule 6.28% 8.18% 4.93% 59Ventricle 4.42% 1.48% 3.63% Whole Brain 5.51% 0.76% 4.76% Region MeanCO2-Challenge Anesthetized Number std Anesthetized std N BaselineBaseline  1 6.88% 2.76% 6.85% 12 −0.08% 0.31%  2 1.01% 3.80% 1.12% 120.40% −0.04%  3 0.79% 5.98% 0.94% 12 0.08% 0.04%  4 1.17% 5.72% 0.94% 120.95% 0.22%  5 0.97% 6.41% 1.02% 12 0.97% −0.20%  6 0.77% 2.85% 0.58% 120.62% 0.01%  7 0.98% 7.02% 0.88% 12 0.64% −0.23  8 0.80% 6.04% 0.58% 120.93% −0.38%  9 8.57% 10.53% 3.16% 12 4.20% −3.96% 10 0.94% 5.88% 0.72%12 0.34% −0.23% 11 0.79% 3.60% 0.68% 12 0.79% −0.07% 12 0.71% 3.57%0.68% 12 0.63% −0.08% 13 1.11% 7.68% 1.55% 12 0.27% −0.07 14 0.85% 3.66%0.66% 12 0.68% 0.00% 15 7.13% 2.67% 6.89% 12 0.14% −0.57% 16 0.79% 2.60%0.80% 12 0.96% 0.15% 17 0.93% 4.06% 0.67% 12 0.64% −0.29% 18 1.16% 5.70%0.95% 12 0.36% −0.02% 19 1.04% 6.64% 0.98% 12 0.51% 0.44% 20 0.83% 2.41%0.65% 12 0.95% −0.20% 21 0.92% 1.59% 0.74% 12 0.44% −0.27% 22 0.84%1.04% 0.54% 12 1.10% 0.16% 23 1.10% 7.53% 0.92% 12 0.51% 0.21% 24 1.45%9.07% 1.87% 12 0.14% 0.36% 25 0.78% 4.18% 0.97% 12 0.40% 0.43% 26 1.77%5.61% 1.15% 12 0.92% 0.59% 27 1.24% 6.41% 1.45% 12 0.79% −0.12% 28 0.75%1.48% 0.62% 12 0.81% 0.28% 29 0.84% 6.69% 1.38% 12 0.60% −1.22% 30 0.87%6.15% 0.85% 12 0.55% 0.05% 31 0.79% 2.08% 0.41% 12 1.03% 0.11% 32 1.04%6.44% 0.92% 12 0.63% −0.13% 33 1.70% 8.63% 1.23% 12 0.24% −0.65% 340.69% 3.95% 0.78% 12 0.69% −0.02% 35 0.79% 4.62% 0.61% 12 0.65% −0.03%36 0.82% 7.19% 0.99% 12 0.45% −0.48% 37 2.37% 10.73% 1.54% 12 −0.10%−0.23% 38 1.27% 11.46% 1.69% 12 0.11% −0.76% 39 1.25% 5.32% 1.33% 120.82% 0.49% 40 3.34% 11.85% 7.84% 12 1.91% −3.16% 41 1.60% 9.53% 1.56%12 0.27% −1.24% 42 1.75% 11.88% 1.86% 12 0.62% 0.35% 43 0.71% 3.42%0.61% 12 1.04% 0.05% 44 1.35% 6.29% 1.44% 12 0.60% 0.08% 45 1.96% 10.25%1.58% 12 0.64% −1.07% 46 0.82% 4.63% 0.67% 12 1.19% −0.19% 47 0.74%3.35% 0.60% 12 0.97% −0.24% 48 0.84% 3.14% 0.68% 12 1.28% 0.07% 49 0.70%4.39% 0.78% 12 1.15% −0.27% 50 0.88% 4.85% 0.74% 12 1.24% −0.25% 511.29% 2.49% 0.91% 12 0.83% 0.14% 52 1.27% 6.86% 1.23% 12 0.11% 0.18% 531.07% 3.18% 1.12% 12 1.00% 0.61% 54 1.06% 7.98% 0.94% 12 0.72% −0.41% 550.61% 7.83% 0.77% 12 0.94% −1.23 56 1.04% 6.35% 1.39% 12 0.89% 0.13% 571.96% 9.81% 1.57% 12 0.68% −0.28% 58 7.60% 5.17% 7.53% 12 1.35 0.24% 592.79% 3.79% 1.53% 12 0.79% 0.16% 0.75% 4.69% 0.64% 12 0.75% −0.07%

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What is claimed is:
 1. A method of positive-contrast magnetic resonanceimaging of a subject, comprising: introducing a paramagnetic orsuperparamagnetic contrast agent into a region of interest in thesubject; applying a magnetic field to the region of interest; applying aradio frequency pulse sequence at a selected repetition time (TR) and ata magnetic field gradient to provide a selected flip angle to exciteprotons in the region of interest, wherein the repetition time is lessthan about 10 ms, and the flip angle ranges from about 10° to about 30°;measuring a response signal during relaxation of the protons at aselected time to echo (TE) to acquire a T_(i)-weighted signal from theregion of interest, wherein the time to echo is an ultra-short time toecho less than about 300 μs; and generating an image of the region ofinterest.
 2. The method of claim 1, wherein the acquired signal isrepresentative of a concentration of the contrast agent in the region ofinterest.
 3. The method of claim 1, wherein the acquired signal isrepresentative of a blood volume in the region of interest.
 4. Themethod of claim 1, wherein the acquired signal comprises an absolutequantitative signal.
 5. The method of claim 1, further comprisingsetting the time to echo (TE) to a value from about 1 us to about 300μs.
 6. The method of claim 1, further comprising setting the time toecho (TE) to less than a time in which blood volume displacement in thevascular region is about one order of magnitude smaller than a voxelsize.
 7. The method of claim 1, further comprising setting therepetition time (TR) to a value from about 2 to about 10 ms.
 8. Themethod of claim 1, further comprising setting the flip angle to a valuefrom about 10° to about 25°.
 9. The method of claim 1, wherein the imageof the region of interest has a contrast to noise ratio of at least 4.10. The method of claim 1, further comprising measuring the responsesignal along radial trajectories in k-space.
 11. The method of claim 1,further comprising acquiring a purely T₁-weighted signal.
 12. The methodof claim 1, wherein the magnetic field has a strength ranging from about0.2 T to about 14.0 T.
 13. The method of claim 1, wherein the region ofinterest comprises a volume fraction occupied by blood and a volumefraction occupied by tissue; and further comprising determining thevolume fraction occupied by blood.
 14. The method of claim 1, whereinthe paramagnetic nanoparticles comprise iron oxide nanoparticles, agadolinium chelate, or a gadolinium compound.
 15. The method of claim14, wherein the iron oxide nanoparticles comprise a material selectedfrom the group consisting of Fe₃O₄ (magnetite), γ-Fe₂O₃ (maghemite),α-Fe₂O₃ (hematite), ferumoxytol, ferumoxides, ferucarbotran, andferumoxtran.
 16. The method of claim 14, wherein the gadolinium compoundis selected from the group consisting of gadofosveset trisodium,gadoterate meglumine, gadoxetic acid disodium salt, gadobutrol,gadopentetic dimeglumine, gadobenate dimeglumine, gadodiamide,gadoversetamide, and gadoteridol.
 17. The method of claim 1, furthercomprising calibrating a magnetic resonance imaging device to determinethe selected TR, the selected TE, and a selected flip angle.
 18. Themethod of claim 1, wherein an intensity of the acquired signal is afunction of one or more of a time to echo (TE), a repetition time (TR),a flip angle (θ), a longitudinal relaxation time T₁, a transverserelaxation time T₂*, a calibration constant K dependent on a coil of themagnetic resonance imaging device, a proton density ρ of the region ofinterest, and magnetic flux densities B₀ and B₁ (+/−).
 19. The method ofclaim 1, wherein the region of interest is a vascular region, a tissuecompartment, an extracellular space, or an intracellular spacecontaining the contrast agent.
 20. A system for magnetic resonanceimaging of a region of interest of a subject, comprising: a magneticresonance imaging device operative to generate signals for forming amagnetic resonance image of the region of interest, and one or moreprocessors and memory, and computer-executable instructions stored inthe memory that, upon execution by the one or more processors, cause thesystem to carry out operations, comprising: operating the magneticresonance imaging device with a radio frequency pulse sequencecomprising: a selected repetition time (TR) and at a magnetic fieldgradient to provide a selected flip angle to excite protons in theregion of interest within a magnetic field generated by the magneticresonance device, wherein the repetition time is less than about 10 ms,and the flip angle ranges from about 10° to about 30°, and a selectedtime to echo (TE) to acquire a T₁-weighted signal from the region ofinterest, wherein the time to echo is an ultrashort time to echo lessthan about 300 μs.